RL4J: Add Backwardly Compatible Builder patterns (#326)

* Starting to switch configs of RL algorithms to use more fluent builder patterns. Many parameter choices in different algorithms default to SOTA and only be changed in specific cases

Signed-off-by: Bam4d <chris.bam4d@gmail.com>

* remove personal gpu-build file

Signed-off-by: Bam4d <chrisbam4d@gmail.com>

* refactored out configurations so they are heirarchical and re-usable, this is a step towards having a plug-and-play framework for different algorithms

* backwardly compatible configurations

* adding documentation to new configuration classes

Signed-off-by: Bam4d <chrisbam4d@gmail.com>

* private access modifiers are better suited here

Signed-off-by: Bam4d <chrisbam4d@gmail.com>

* RL4j does not compile without java 8 due to previous updates

fixing null pointers when listener arrays are empty

Signed-off-by: Bam4d <chrisbam4d@gmail.com>

* fixing copyright headers

Signed-off-by: Bam4d <chrisbam4d@gmail.com>

* uncomment logging line

Signed-off-by: Bam4d <chrisbam4d@gmail.com>

* fixing default value for learningUpdateFrequency

fixing test failure due to #352

Signed-off-by: Bam4d <chrisbam4d@gmail.com>

Co-authored-by: Bam4d <chris.bam4d@gmail.com>

datavec/datavec-api/src/main/java/org/datavec/api/transform/split/RandomSplit.java

@@ -16,6 +16,7 @@
 
 package org.datavec.api.transform.split;
 
+
 import lombok.AllArgsConstructor;
 import lombok.Data;
 

rl4j/rl4j-core/pom.xml

@@ -20,6 +20,18 @@
          xmlns="http://maven.apache.org/POM/4.0.0"
          xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
     <modelVersion>4.0.0</modelVersion>
+    <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-compiler-plugin</artifactId>
+                <configuration>
+                    <source>8</source>
+                    <target>8</target>
+                </configuration>
+            </plugin>
+        </plugins>
+    </build>
 
     <parent>
         <artifactId>rl4j</artifactId>

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/EpochStepCounter.java

@@ -1,3 +1,19 @@
+/*******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
 package org.deeplearning4j.rl4j.learning;
 
 public interface EpochStepCounter {

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/ILearning.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -16,10 +17,10 @@
 
 package org.deeplearning4j.rl4j.learning;
 
+import org.deeplearning4j.rl4j.learning.configuration.ILearningConfiguration;
 import org.deeplearning4j.rl4j.mdp.MDP;
 import org.deeplearning4j.rl4j.policy.IPolicy;
 import org.deeplearning4j.rl4j.space.ActionSpace;
-import org.deeplearning4j.rl4j.space.Encodable;
 
 /**
  * @author rubenfiszel (ruben.fiszel@epfl.ch) 7/19/16.
@@ -34,21 +35,12 @@ public interface ILearning<O, A, AS extends ActionSpace<A>> {
 
     int getStepCounter();
 
-    LConfiguration getConfiguration();
+    ILearningConfiguration getConfiguration();
 
     MDP<O, A, AS> getMdp();
 
     IHistoryProcessor getHistoryProcessor();
 
-    interface LConfiguration {
 
-        Integer getSeed();
-
-        int getMaxEpochStep();
-
-        int getMaxStep();
-
-        double getGamma();
-    }
 
 }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/AsyncGlobal.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -19,6 +20,8 @@ package org.deeplearning4j.rl4j.learning.async;
 import lombok.Getter;
 import lombok.extern.slf4j.Slf4j;
 import org.deeplearning4j.nn.gradient.Gradient;
+import org.deeplearning4j.rl4j.learning.configuration.AsyncQLearningConfiguration;
+import org.deeplearning4j.rl4j.learning.configuration.IAsyncLearningConfiguration;
 import org.deeplearning4j.rl4j.network.NeuralNet;
 import org.nd4j.linalg.primitives.Pair;
 
@@ -27,28 +30,26 @@ import java.util.concurrent.atomic.AtomicInteger;
 
 /**
  * @author rubenfiszel (ruben.fiszel@epfl.ch) on 8/5/16.
- *
+ * <p>
  * In the original paper, the authors uses Asynchronous
  * Gradient Descent: Hogwild! It is a way to apply gradients
  * and modify a model in a lock-free manner.
- *
+ * <p>
  * As a way to implement this with dl4j, it is unfortunately
  * necessary at the time of writing to apply the gradient
  * (update the parameters) on a single separate global thread.
- *
+ * <p>
  * This Central thread for Asynchronous Method of reinforcement learning
  * enqueue the gradients coming from the different threads and update its
  * model and target. Those neurals nets are then synced by the other threads.
- *
+ * <p>
  * The benefits of this thread is that the updater is "shared" between all thread
  * we have a single updater which is the single updater of the model contained here
- *
+ * <p>
  * This is similar to RMSProp with shared g and momentum
- *
+ * <p>
  * When Hogwild! is implemented, this could be replaced by a simple data
  * structure
- *
- *
  */
 @Slf4j
 public class AsyncGlobal<NN extends NeuralNet> extends Thread implements IAsyncGlobal<NN> {
@@ -56,7 +57,7 @@ public class AsyncGlobal<NN extends NeuralNet> extends Thread implements IAsyncG
     @Getter
     final private NN current;
     final private ConcurrentLinkedQueue<Pair<Gradient[], Integer>> queue;
-    final private AsyncConfiguration a3cc;
+    final private IAsyncLearningConfiguration configuration;
     private final IAsyncLearning learning;
     @Getter
     private AtomicInteger T = new AtomicInteger(0);
@@ -65,20 +66,20 @@ public class AsyncGlobal<NN extends NeuralNet> extends Thread implements IAsyncG
     @Getter
     private boolean running = true;
 
-    public AsyncGlobal(NN initial, AsyncConfiguration a3cc, IAsyncLearning learning) {
+    public AsyncGlobal(NN initial, IAsyncLearningConfiguration configuration, IAsyncLearning learning) {
         this.current = initial;
         target = (NN) initial.clone();
-        this.a3cc = a3cc;
+        this.configuration = configuration;
         this.learning = learning;
         queue = new ConcurrentLinkedQueue<>();
     }
 
     public boolean isTrainingComplete() {
-        return T.get() >= a3cc.getMaxStep();
+        return T.get() >= configuration.getMaxStep();
     }
 
     public void enqueue(Gradient[] gradient, Integer nstep) {
-        if(running && !isTrainingComplete()) {
+        if (running && !isTrainingComplete()) {
             queue.add(new Pair<>(gradient, nstep));
         }
     }
@@ -94,9 +95,8 @@ public class AsyncGlobal<NN extends NeuralNet> extends Thread implements IAsyncG
                 synchronized (this) {
                     current.applyGradient(gradient, pair.getSecond());
                 }
-                if (a3cc.getTargetDqnUpdateFreq() != -1
+                if (configuration.getLearnerUpdateFrequency() != -1  && T.get() / configuration.getLearnerUpdateFrequency() > (T.get() - pair.getSecond())
-                                && T.get() / a3cc.getTargetDqnUpdateFreq() > (T.get() - pair.getSecond())
+                        / configuration.getLearnerUpdateFrequency()) {
-                                                / a3cc.getTargetDqnUpdateFreq()) {
                     log.info("TARGET UPDATE at T = " + T.get());
                     synchronized (this) {
                         target.copy(current);
@@ -111,7 +111,7 @@ public class AsyncGlobal<NN extends NeuralNet> extends Thread implements IAsyncG
      * Force the immediate termination of the AsyncGlobal instance. Queued work items will be discarded and the AsyncLearning instance will be forced to terminate too.
      */
     public void terminate() {
-        if(running) {
+        if (running) {
             running = false;
             queue.clear();
             learning.terminate();

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/AsyncLearning.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -21,14 +22,17 @@ import lombok.Getter;
 import lombok.Setter;
 import lombok.extern.slf4j.Slf4j;
 import org.deeplearning4j.rl4j.learning.Learning;
-import org.deeplearning4j.rl4j.learning.listener.*;
+import org.deeplearning4j.rl4j.learning.configuration.AsyncQLearningConfiguration;
+import org.deeplearning4j.rl4j.learning.configuration.IAsyncLearningConfiguration;
+import org.deeplearning4j.rl4j.learning.listener.TrainingListener;
+import org.deeplearning4j.rl4j.learning.listener.TrainingListenerList;
 import org.deeplearning4j.rl4j.network.NeuralNet;
 import org.deeplearning4j.rl4j.space.ActionSpace;
 import org.deeplearning4j.rl4j.space.Encodable;
 import org.nd4j.linalg.factory.Nd4j;
 
 /**
- * The entry point for async training. This class will start a number ({@link AsyncConfiguration#getNumThread()
+ * The entry point for async training. This class will start a number ({@link AsyncQLearningConfiguration#getNumThreads()
  * configuration.getNumThread()}) of worker threads. Then, it will monitor their progress at regular intervals
  * (see setProgressEventInterval(int))
  *
@@ -37,8 +41,8 @@ import org.nd4j.linalg.factory.Nd4j;
  */
 @Slf4j
 public abstract class AsyncLearning<O extends Encodable, A, AS extends ActionSpace<A>, NN extends NeuralNet>
-                extends Learning<O, A, AS, NN>
+        extends Learning<O, A, AS, NN>
-                implements IAsyncLearning {
+        implements IAsyncLearning {
 
     private Thread monitorThread = null;
 
@@ -56,9 +60,10 @@ public abstract class AsyncLearning<O extends Encodable, A, AS extends ActionSpa
 
     /**
      * Returns the configuration
-     * @return the configuration (see {@link AsyncConfiguration})
+     *
+     * @return the configuration (see {@link AsyncQLearningConfiguration})
      */
-    public abstract AsyncConfiguration getConfiguration();
+    public abstract IAsyncLearningConfiguration getConfiguration();
 
     protected abstract AsyncThread newThread(int i, int deviceAffinity);
 
@@ -77,12 +82,13 @@ public abstract class AsyncLearning<O extends Encodable, A, AS extends ActionSpa
     /**
      * Number of milliseconds between calls to onTrainingProgress
      */
-    @Getter @Setter
+    @Getter
+    @Setter
     private int progressMonitorFrequency = 20000;
 
     private void launchThreads() {
         startGlobalThread();
-        for (int i = 0; i < getConfiguration().getNumThread(); i++) {
+        for (int i = 0; i < getConfiguration().getNumThreads(); i++) {
             Thread t = newThread(i, i % Nd4j.getAffinityManager().getNumberOfDevices());
             t.start();
         }
@@ -132,7 +138,7 @@ public abstract class AsyncLearning<O extends Encodable, A, AS extends ActionSpa
             monitorThread = Thread.currentThread();
             while (canContinue && !isTrainingComplete() && getAsyncGlobal().isRunning()) {
                 canContinue = listeners.notifyTrainingProgress(this);
-                if(!canContinue) {
+                if (!canContinue) {
                     return;
                 }
 
@@ -155,11 +161,11 @@ public abstract class AsyncLearning<O extends Encodable, A, AS extends ActionSpa
      * Force the immediate termination of the learning. All learning threads, the AsyncGlobal thread and the monitor thread will be terminated.
      */
     public void terminate() {
-        if(canContinue) {
+        if (canContinue) {
             canContinue = false;
 
             Thread safeMonitorThread = monitorThread;
-            if(safeMonitorThread != null) {
+            if (safeMonitorThread != null) {
                 safeMonitorThread.interrupt();
             }
         }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/AsyncThread.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -23,6 +24,7 @@ import lombok.Value;
 import lombok.extern.slf4j.Slf4j;
 import org.deeplearning4j.gym.StepReply;
 import org.deeplearning4j.rl4j.learning.*;
+import org.deeplearning4j.rl4j.learning.configuration.IAsyncLearningConfiguration;
 import org.deeplearning4j.rl4j.learning.listener.TrainingListener;
 import org.deeplearning4j.rl4j.learning.listener.TrainingListenerList;
 import org.deeplearning4j.rl4j.mdp.MDP;
@@ -155,7 +157,7 @@ public abstract class AsyncThread<O, A, AS extends ActionSpace<A>, NN extends Ne
     }
 
     private void handleTraining(RunContext context) {
-        int maxSteps = Math.min(getConf().getNstep(), getConf().getMaxEpochStep() - currentEpochStep);
+        int maxSteps = Math.min(getConf().getNStep(), getConf().getMaxEpochStep() - currentEpochStep);
         SubEpochReturn subEpochReturn = trainSubEpoch(context.obs, maxSteps);
 
         context.obs = subEpochReturn.getLastObs();
@@ -197,7 +199,7 @@ public abstract class AsyncThread<O, A, AS extends ActionSpace<A>, NN extends Ne
 
     protected abstract IAsyncGlobal<NN> getAsyncGlobal();
 
-    protected abstract AsyncConfiguration getConf();
+    protected abstract IAsyncLearningConfiguration getConf();
 
     protected abstract IPolicy<O, A> getPolicy(NN net);
 

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/AsyncThreadDiscrete.java

@@ -1,5 +1,7 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
+ *
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -112,7 +114,7 @@ public abstract class AsyncThreadDiscrete<O, NN extends NeuralNet>
             rewards.add(new MiniTrans(obs.getData(), null, null, 0));
         else {
             INDArray[] output = null;
-            if (getConf().getTargetDqnUpdateFreq() == -1)
+            if (getConf().getLearnerUpdateFrequency() == -1)
                 output = current.outputAll(obs.getData());
             else synchronized (getAsyncGlobal()) {
                 output = getAsyncGlobal().getTarget().outputAll(obs.getData());

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/a3c/discrete/A3CDiscrete.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -16,11 +17,15 @@
 
 package org.deeplearning4j.rl4j.learning.async.a3c.discrete;
 
-import lombok.*;
+import lombok.AllArgsConstructor;
-import org.deeplearning4j.rl4j.learning.async.AsyncConfiguration;
+import lombok.Builder;
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+import lombok.Getter;
 import org.deeplearning4j.rl4j.learning.async.AsyncGlobal;
 import org.deeplearning4j.rl4j.learning.async.AsyncLearning;
 import org.deeplearning4j.rl4j.learning.async.AsyncThread;
+import org.deeplearning4j.rl4j.learning.configuration.A3CLearningConfiguration;
 import org.deeplearning4j.rl4j.mdp.MDP;
 import org.deeplearning4j.rl4j.network.ac.IActorCritic;
 import org.deeplearning4j.rl4j.policy.ACPolicy;
@@ -32,15 +37,14 @@ import org.nd4j.linalg.factory.Nd4j;
 /**
  * @author rubenfiszel (ruben.fiszel@epfl.ch) 7/23/16.
  * Training for A3C in the Discrete Domain
- *
+ * <p>
  * All methods are fully implemented as described in the
  * https://arxiv.org/abs/1602.01783 paper.
- *
  */
 public abstract class A3CDiscrete<O extends Encodable> extends AsyncLearning<O, Integer, DiscreteSpace, IActorCritic> {
 
     @Getter
-    final public A3CConfiguration configuration;
+    final public A3CLearningConfiguration configuration;
     @Getter
     final protected MDP<O, Integer, DiscreteSpace> mdp;
     final private IActorCritic iActorCritic;
@@ -49,15 +53,15 @@ public abstract class A3CDiscrete<O extends Encodable> extends AsyncLearning<O,
     @Getter
     final private ACPolicy<O> policy;
 
-    public A3CDiscrete(MDP<O, Integer, DiscreteSpace> mdp, IActorCritic iActorCritic, A3CConfiguration conf) {
+    public A3CDiscrete(MDP<O, Integer, DiscreteSpace> mdp, IActorCritic iActorCritic, A3CLearningConfiguration conf) {
         this.iActorCritic = iActorCritic;
         this.mdp = mdp;
         this.configuration = conf;
         asyncGlobal = new AsyncGlobal<>(iActorCritic, conf, this);
 
-        Integer seed = conf.getSeed();
+        Long seed = conf.getSeed();
         Random rnd = Nd4j.getRandom();
-        if(seed != null) {
+        if (seed != null) {
             rnd.setSeed(seed);
         }
 
@@ -65,7 +69,7 @@ public abstract class A3CDiscrete<O extends Encodable> extends AsyncLearning<O,
     }
 
     protected AsyncThread newThread(int i, int deviceNum) {
-        return new A3CThreadDiscrete(mdp.newInstance(), asyncGlobal, getConfiguration(), deviceNum, getListeners(), i);
+        return new A3CThreadDiscrete(mdp.newInstance(), asyncGlobal, this.getConfiguration(), deviceNum, getListeners(), i);
     }
 
     public IActorCritic getNeuralNet() {
@@ -76,9 +80,9 @@ public abstract class A3CDiscrete<O extends Encodable> extends AsyncLearning<O,
     @AllArgsConstructor
     @Builder
     @EqualsAndHashCode(callSuper = false)
-    public static class A3CConfiguration implements AsyncConfiguration {
+    public static class A3CConfiguration {
 
-        Integer seed;
+        int seed;
         int maxEpochStep;
         int maxStep;
         int numThread;
@@ -88,8 +92,20 @@ public abstract class A3CDiscrete<O extends Encodable> extends AsyncLearning<O,
         double gamma;
         double errorClamp;
 
-        public int getTargetDqnUpdateFreq() {
+        /**
-            return -1;
+         * Converts the deprecated A3CConfiguration to the new LearningConfiguration format
+         */
+        public A3CLearningConfiguration toLearningConfiguration() {
+            return A3CLearningConfiguration.builder()
+                    .seed(new Long(seed))
+                    .maxEpochStep(maxEpochStep)
+                    .maxStep(maxStep)
+                    .numThreads(numThread)
+                    .nStep(nstep)
+                    .rewardFactor(rewardFactor)
+                    .gamma(gamma)
+                    .build();
+
         }
 
     }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/a3c/discrete/A3CDiscreteConv.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -18,10 +19,12 @@ package org.deeplearning4j.rl4j.learning.async.a3c.discrete;
 
 import org.deeplearning4j.rl4j.learning.HistoryProcessor;
 import org.deeplearning4j.rl4j.learning.async.AsyncThread;
+import org.deeplearning4j.rl4j.learning.configuration.A3CLearningConfiguration;
 import org.deeplearning4j.rl4j.mdp.MDP;
 import org.deeplearning4j.rl4j.network.ac.ActorCriticFactoryCompGraph;
 import org.deeplearning4j.rl4j.network.ac.ActorCriticFactoryCompGraphStdConv;
 import org.deeplearning4j.rl4j.network.ac.IActorCritic;
+import org.deeplearning4j.rl4j.network.configuration.ActorCriticNetworkConfiguration;
 import org.deeplearning4j.rl4j.space.DiscreteSpace;
 import org.deeplearning4j.rl4j.space.Encodable;
 import org.deeplearning4j.rl4j.util.DataManagerTrainingListener;
@@ -29,16 +32,15 @@ import org.deeplearning4j.rl4j.util.IDataManager;
 
 /**
  * @author rubenfiszel (ruben.fiszel@epfl.ch) on 8/8/16.
- *
+ * <p>
  * Training for A3C in the Discrete Domain
- *
+ * <p>
  * Specialized constructors for the Conv (pixels input) case
  * Specialized conf + provide additional type safety
- *
+ * <p>
  * It uses CompGraph because there is benefit to combine the
  * first layers since they're essentially doing the same dimension
  * reduction task
- *
  **/
 public class A3CDiscreteConv<O extends Encodable> extends A3CDiscrete<O> {
 
@@ -46,12 +48,22 @@ public class A3CDiscreteConv<O extends Encodable> extends A3CDiscrete<O> {
 
     @Deprecated
     public A3CDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, IActorCritic actorCritic,
-                    HistoryProcessor.Configuration hpconf, A3CConfiguration conf, IDataManager dataManager) {
+                           HistoryProcessor.Configuration hpconf, A3CConfiguration conf, IDataManager dataManager) {
         this(mdp, actorCritic, hpconf, conf);
         addListener(new DataManagerTrainingListener(dataManager));
     }
+
+    @Deprecated
     public A3CDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, IActorCritic IActorCritic,
                            HistoryProcessor.Configuration hpconf, A3CConfiguration conf) {
+
+        super(mdp, IActorCritic, conf.toLearningConfiguration());
+        this.hpconf = hpconf;
+        setHistoryProcessor(hpconf);
+    }
+
+    public A3CDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, IActorCritic IActorCritic,
+                           HistoryProcessor.Configuration hpconf, A3CLearningConfiguration conf) {
         super(mdp, IActorCritic, conf);
         this.hpconf = hpconf;
         setHistoryProcessor(hpconf);
@@ -59,21 +71,35 @@ public class A3CDiscreteConv<O extends Encodable> extends A3CDiscrete<O> {
 
     @Deprecated
     public A3CDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactoryCompGraph factory,
-                    HistoryProcessor.Configuration hpconf, A3CConfiguration conf, IDataManager dataManager) {
+                           HistoryProcessor.Configuration hpconf, A3CConfiguration conf, IDataManager dataManager) {
         this(mdp, factory.buildActorCritic(hpconf.getShape(), mdp.getActionSpace().getSize()), hpconf, conf, dataManager);
     }
+
+    @Deprecated
     public A3CDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactoryCompGraph factory,
                            HistoryProcessor.Configuration hpconf, A3CConfiguration conf) {
         this(mdp, factory.buildActorCritic(hpconf.getShape(), mdp.getActionSpace().getSize()), hpconf, conf);
     }
 
+    public A3CDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactoryCompGraph factory,
+                           HistoryProcessor.Configuration hpconf, A3CLearningConfiguration conf) {
+        this(mdp, factory.buildActorCritic(hpconf.getShape(), mdp.getActionSpace().getSize()), hpconf, conf);
+    }
+
     @Deprecated
     public A3CDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactoryCompGraphStdConv.Configuration netConf,
-                    HistoryProcessor.Configuration hpconf, A3CConfiguration conf, IDataManager dataManager) {
+                           HistoryProcessor.Configuration hpconf, A3CConfiguration conf, IDataManager dataManager) {
-        this(mdp, new ActorCriticFactoryCompGraphStdConv(netConf), hpconf, conf, dataManager);
+        this(mdp, new ActorCriticFactoryCompGraphStdConv(netConf.toNetworkConfiguration()), hpconf, conf, dataManager);
     }
+
+    @Deprecated
     public A3CDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactoryCompGraphStdConv.Configuration netConf,
                            HistoryProcessor.Configuration hpconf, A3CConfiguration conf) {
+        this(mdp, new ActorCriticFactoryCompGraphStdConv(netConf.toNetworkConfiguration()), hpconf, conf);
+    }
+
+    public A3CDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticNetworkConfiguration netConf,
+                           HistoryProcessor.Configuration hpconf, A3CLearningConfiguration conf) {
         this(mdp, new ActorCriticFactoryCompGraphStdConv(netConf), hpconf, conf);
     }
 

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/a3c/discrete/A3CDiscreteDense.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -16,8 +17,10 @@
 
 package org.deeplearning4j.rl4j.learning.async.a3c.discrete;
 
+import org.deeplearning4j.rl4j.learning.configuration.A3CLearningConfiguration;
 import org.deeplearning4j.rl4j.mdp.MDP;
 import org.deeplearning4j.rl4j.network.ac.*;
+import org.deeplearning4j.rl4j.network.configuration.ActorCriticDenseNetworkConfiguration;
 import org.deeplearning4j.rl4j.space.DiscreteSpace;
 import org.deeplearning4j.rl4j.space.Encodable;
 import org.deeplearning4j.rl4j.util.DataManagerTrainingListener;
@@ -25,67 +28,81 @@ import org.deeplearning4j.rl4j.util.IDataManager;
 
 /**
  * @author rubenfiszel (ruben.fiszel@epfl.ch) on 8/8/16.
- *
+ * <p>
  * Training for A3C in the Discrete Domain
- *
+ * <p>
  * We use specifically the Separate version because
  * the model is too small to have enough benefit by sharing layers
- *
  */
 public class A3CDiscreteDense<O extends Encodable> extends A3CDiscrete<O> {
 
     @Deprecated
     public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, IActorCritic IActorCritic, A3CConfiguration conf,
-                    IDataManager dataManager) {
+                            IDataManager dataManager) {
         this(mdp, IActorCritic, conf);
         addListener(new DataManagerTrainingListener(dataManager));
     }
+
+    @Deprecated
     public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, IActorCritic actorCritic, A3CConfiguration conf) {
+        super(mdp, actorCritic, conf.toLearningConfiguration());
+    }
+
+    public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, IActorCritic actorCritic, A3CLearningConfiguration conf) {
         super(mdp, actorCritic, conf);
     }
 
     @Deprecated
     public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactorySeparate factory,
-                    A3CConfiguration conf, IDataManager dataManager) {
+                            A3CConfiguration conf, IDataManager dataManager) {
         this(mdp, factory.buildActorCritic(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf,
-                        dataManager);
+                dataManager);
     }
+
+    @Deprecated
     public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactorySeparate factory,
                             A3CConfiguration conf) {
         this(mdp, factory.buildActorCritic(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf);
     }
 
-    @Deprecated
+    public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactorySeparate factory,
-    public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp,
+                            A3CLearningConfiguration conf) {
-                    ActorCriticFactorySeparateStdDense.Configuration netConf, A3CConfiguration conf,
-                    IDataManager dataManager) {
-        this(mdp, new ActorCriticFactorySeparateStdDense(netConf), conf, dataManager);
-    }
-    public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp,
-                            ActorCriticFactorySeparateStdDense.Configuration netConf, A3CConfiguration conf) {
-        this(mdp, new ActorCriticFactorySeparateStdDense(netConf), conf);
-    }
-
-    @Deprecated
-    public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactoryCompGraph factory,
-                    A3CConfiguration conf, IDataManager dataManager) {
-        this(mdp, factory.buildActorCritic(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf,
-                        dataManager);
-    }
-    public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactoryCompGraph factory,
-                            A3CConfiguration conf) {
         this(mdp, factory.buildActorCritic(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf);
     }
 
     @Deprecated
     public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp,
-                    ActorCriticFactoryCompGraphStdDense.Configuration netConf, A3CConfiguration conf,
+                            ActorCriticFactorySeparateStdDense.Configuration netConf, A3CConfiguration conf,
-                    IDataManager dataManager) {
+                            IDataManager dataManager) {
-        this(mdp, new ActorCriticFactoryCompGraphStdDense(netConf), conf, dataManager);
+        this(mdp, new ActorCriticFactorySeparateStdDense(netConf.toNetworkConfiguration()), conf, dataManager);
-    }
-    public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp,
-                            ActorCriticFactoryCompGraphStdDense.Configuration netConf, A3CConfiguration conf) {
-        this(mdp, new ActorCriticFactoryCompGraphStdDense(netConf), conf);
     }
 
+    @Deprecated
+    public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp,
+                            ActorCriticFactorySeparateStdDense.Configuration netConf, A3CConfiguration conf) {
+        this(mdp, new ActorCriticFactorySeparateStdDense(netConf.toNetworkConfiguration()), conf);
+    }
+
+    public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp,
+                            ActorCriticDenseNetworkConfiguration netConf, A3CLearningConfiguration conf) {
+        this(mdp, new ActorCriticFactorySeparateStdDense(netConf), conf);
+    }
+
+    @Deprecated
+    public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactoryCompGraph factory,
+                            A3CConfiguration conf, IDataManager dataManager) {
+        this(mdp, factory.buildActorCritic(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf,
+                dataManager);
+    }
+
+    @Deprecated
+    public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactoryCompGraph factory,
+                            A3CConfiguration conf) {
+        this(mdp, factory.buildActorCritic(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf);
+    }
+
+    public A3CDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, ActorCriticFactoryCompGraph factory,
+                            A3CLearningConfiguration conf) {
+        this(mdp, factory.buildActorCritic(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf);
+    }
 }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/a3c/discrete/A3CThreadDiscrete.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -19,10 +20,10 @@ package org.deeplearning4j.rl4j.learning.async.a3c.discrete;
 import lombok.Getter;
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.rl4j.learning.Learning;
-import org.deeplearning4j.rl4j.learning.async.AsyncGlobal;
 import org.deeplearning4j.rl4j.learning.async.AsyncThreadDiscrete;
 import org.deeplearning4j.rl4j.learning.async.IAsyncGlobal;
 import org.deeplearning4j.rl4j.learning.async.MiniTrans;
+import org.deeplearning4j.rl4j.learning.configuration.A3CLearningConfiguration;
 import org.deeplearning4j.rl4j.learning.listener.TrainingListenerList;
 import org.deeplearning4j.rl4j.mdp.MDP;
 import org.deeplearning4j.rl4j.network.ac.IActorCritic;
@@ -31,9 +32,9 @@ import org.deeplearning4j.rl4j.policy.Policy;
 import org.deeplearning4j.rl4j.space.DiscreteSpace;
 import org.deeplearning4j.rl4j.space.Encodable;
 import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.rng.Random;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.indexing.NDArrayIndex;
-import org.nd4j.linalg.api.rng.Random;
 
 import java.util.Stack;
 
@@ -45,7 +46,7 @@ import java.util.Stack;
 public class A3CThreadDiscrete<O extends Encodable> extends AsyncThreadDiscrete<O, IActorCritic> {
 
     @Getter
-    final protected A3CDiscrete.A3CConfiguration conf;
+    final protected A3CLearningConfiguration conf;
     @Getter
     final protected IAsyncGlobal<IActorCritic> asyncGlobal;
     @Getter
@@ -54,14 +55,14 @@ public class A3CThreadDiscrete<O extends Encodable> extends AsyncThreadDiscrete<
     final private Random rnd;
 
     public A3CThreadDiscrete(MDP<O, Integer, DiscreteSpace> mdp, IAsyncGlobal<IActorCritic> asyncGlobal,
-                             A3CDiscrete.A3CConfiguration a3cc, int deviceNum, TrainingListenerList listeners,
+                             A3CLearningConfiguration a3cc, int deviceNum, TrainingListenerList listeners,
                              int threadNumber) {
         super(asyncGlobal, mdp, listeners, threadNumber, deviceNum);
         this.conf = a3cc;
         this.asyncGlobal = asyncGlobal;
         this.threadNumber = threadNumber;
 
-        Integer seed = conf.getSeed();
+        Long seed = conf.getSeed();
         rnd = Nd4j.getRandom();
         if(seed != null) {
             rnd.setSeed(seed + threadNumber);

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/nstep/discrete/AsyncNStepQLearningDiscrete.java

@@ -1,49 +1,53 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
- * This program and the accompanying materials are made available under the
+ *  This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
+ *  terms of the Apache License, Version 2.0 which is available at
- * https://www.apache.org/licenses/LICENSE-2.0.
+ *  https://www.apache.org/licenses/LICENSE-2.0.
  *
- * Unless required by applicable law or agreed to in writing, software
+ *  Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *  distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *  WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations
+ *  License for the specific language governing permissions and limitations
- * under the License.
+ *  under the License.
  *
- * SPDX-License-Identifier: Apache-2.0
+ *  SPDX-License-Identifier: Apache-2.0
  ******************************************************************************/
 
 package org.deeplearning4j.rl4j.learning.async.nstep.discrete;
 
-import lombok.*;
+import lombok.AllArgsConstructor;
-import org.deeplearning4j.rl4j.learning.async.AsyncConfiguration;
+import lombok.Builder;
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+import lombok.Getter;
 import org.deeplearning4j.rl4j.learning.async.AsyncGlobal;
 import org.deeplearning4j.rl4j.learning.async.AsyncLearning;
 import org.deeplearning4j.rl4j.learning.async.AsyncThread;
+import org.deeplearning4j.rl4j.learning.configuration.AsyncQLearningConfiguration;
 import org.deeplearning4j.rl4j.mdp.MDP;
 import org.deeplearning4j.rl4j.network.dqn.IDQN;
 import org.deeplearning4j.rl4j.policy.DQNPolicy;
 import org.deeplearning4j.rl4j.policy.IPolicy;
 import org.deeplearning4j.rl4j.space.DiscreteSpace;
 import org.deeplearning4j.rl4j.space.Encodable;
-import org.nd4j.linalg.factory.Nd4j;
 
 /**
  * @author rubenfiszel (ruben.fiszel@epfl.ch) on 8/5/16.
  */
 public abstract class AsyncNStepQLearningDiscrete<O extends Encodable>
-                extends AsyncLearning<O, Integer, DiscreteSpace, IDQN> {
+        extends AsyncLearning<O, Integer, DiscreteSpace, IDQN> {
 
     @Getter
-    final public AsyncNStepQLConfiguration configuration;
+    final public AsyncQLearningConfiguration configuration;
     @Getter
     final private MDP<O, Integer, DiscreteSpace> mdp;
     @Getter
     final private AsyncGlobal<IDQN> asyncGlobal;
 
 
-    public AsyncNStepQLearningDiscrete(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, AsyncNStepQLConfiguration conf) {
+    public AsyncNStepQLearningDiscrete(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, AsyncQLearningConfiguration conf) {
         this.mdp = mdp;
         this.configuration = conf;
         this.asyncGlobal = new AsyncGlobal<>(dqn, conf, this);
@@ -62,12 +66,11 @@ public abstract class AsyncNStepQLearningDiscrete<O extends Encodable>
         return new DQNPolicy<O>(getNeuralNet());
     }
 
-
     @Data
     @AllArgsConstructor
     @Builder
     @EqualsAndHashCode(callSuper = false)
-    public static class AsyncNStepQLConfiguration implements AsyncConfiguration {
+    public static class AsyncNStepQLConfiguration {
 
         Integer seed;
         int maxEpochStep;
@@ -82,5 +85,22 @@ public abstract class AsyncNStepQLearningDiscrete<O extends Encodable>
         float minEpsilon;
         int epsilonNbStep;
 
+        public AsyncQLearningConfiguration toLearningConfiguration() {
+            return AsyncQLearningConfiguration.builder()
+                    .seed(new Long(seed))
+                    .maxEpochStep(maxEpochStep)
+                    .maxStep(maxStep)
+                    .numThreads(numThread)
+                    .nStep(nstep)
+                    .targetDqnUpdateFreq(targetDqnUpdateFreq)
+                    .updateStart(updateStart)
+                    .rewardFactor(rewardFactor)
+                    .gamma(gamma)
+                    .errorClamp(errorClamp)
+                    .minEpsilon(minEpsilon)
+                    .build();
+        }
+
     }
+
 }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/nstep/discrete/AsyncNStepQLearningDiscreteConv.java

@@ -1,24 +1,27 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
- * This program and the accompanying materials are made available under the
+ *  This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
+ *  terms of the Apache License, Version 2.0 which is available at
- * https://www.apache.org/licenses/LICENSE-2.0.
+ *  https://www.apache.org/licenses/LICENSE-2.0.
  *
- * Unless required by applicable law or agreed to in writing, software
+ *  Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *  distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *  WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations
+ *  License for the specific language governing permissions and limitations
- * under the License.
+ *  under the License.
  *
- * SPDX-License-Identifier: Apache-2.0
+ *  SPDX-License-Identifier: Apache-2.0
  ******************************************************************************/
 
 package org.deeplearning4j.rl4j.learning.async.nstep.discrete;
 
 import org.deeplearning4j.rl4j.learning.HistoryProcessor;
 import org.deeplearning4j.rl4j.learning.async.AsyncThread;
+import org.deeplearning4j.rl4j.learning.configuration.AsyncQLearningConfiguration;
 import org.deeplearning4j.rl4j.mdp.MDP;
+import org.deeplearning4j.rl4j.network.configuration.NetworkConfiguration;
 import org.deeplearning4j.rl4j.network.dqn.DQNFactory;
 import org.deeplearning4j.rl4j.network.dqn.DQNFactoryStdConv;
 import org.deeplearning4j.rl4j.network.dqn.IDQN;
@@ -38,12 +41,12 @@ public class AsyncNStepQLearningDiscreteConv<O extends Encodable> extends AsyncN
 
     @Deprecated
     public AsyncNStepQLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn,
-                    HistoryProcessor.Configuration hpconf, AsyncNStepQLConfiguration conf, IDataManager dataManager) {
+                                           HistoryProcessor.Configuration hpconf, AsyncQLearningConfiguration conf, IDataManager dataManager) {
         this(mdp, dqn, hpconf, conf);
         addListener(new DataManagerTrainingListener(dataManager));
     }
     public AsyncNStepQLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn,
-                                           HistoryProcessor.Configuration hpconf, AsyncNStepQLConfiguration conf) {
+                                           HistoryProcessor.Configuration hpconf, AsyncQLearningConfiguration conf) {
         super(mdp, dqn, conf);
         this.hpconf = hpconf;
         setHistoryProcessor(hpconf);
@@ -51,21 +54,21 @@ public class AsyncNStepQLearningDiscreteConv<O extends Encodable> extends AsyncN
 
     @Deprecated
     public AsyncNStepQLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, DQNFactory factory,
-                    HistoryProcessor.Configuration hpconf, AsyncNStepQLConfiguration conf, IDataManager dataManager) {
+                    HistoryProcessor.Configuration hpconf, AsyncQLearningConfiguration conf, IDataManager dataManager) {
         this(mdp, factory.buildDQN(hpconf.getShape(), mdp.getActionSpace().getSize()), hpconf, conf, dataManager);
     }
     public AsyncNStepQLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, DQNFactory factory,
-                                           HistoryProcessor.Configuration hpconf, AsyncNStepQLConfiguration conf) {
+                                           HistoryProcessor.Configuration hpconf, AsyncQLearningConfiguration conf) {
         this(mdp, factory.buildDQN(hpconf.getShape(), mdp.getActionSpace().getSize()), hpconf, conf);
     }
 
     @Deprecated
-    public AsyncNStepQLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, DQNFactoryStdConv.Configuration netConf,
+    public AsyncNStepQLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, NetworkConfiguration netConf,
-                    HistoryProcessor.Configuration hpconf, AsyncNStepQLConfiguration conf, IDataManager dataManager) {
+                    HistoryProcessor.Configuration hpconf, AsyncQLearningConfiguration conf, IDataManager dataManager) {
         this(mdp, new DQNFactoryStdConv(netConf), hpconf, conf, dataManager);
     }
-    public AsyncNStepQLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, DQNFactoryStdConv.Configuration netConf,
+    public AsyncNStepQLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, NetworkConfiguration netConf,
-                                           HistoryProcessor.Configuration hpconf, AsyncNStepQLConfiguration conf) {
+                                           HistoryProcessor.Configuration hpconf, AsyncQLearningConfiguration conf) {
         this(mdp, new DQNFactoryStdConv(netConf), hpconf, conf);
     }
 

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/nstep/discrete/AsyncNStepQLearningDiscreteDense.java

@@ -1,22 +1,26 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://www.apache.org/licenses/LICENSE-2.0.
  *
- * Unless required by applicable law or agreed to in writing, software
+ *  This program and the accompanying materials are made available under the
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *  terms of the Apache License, Version 2.0 which is available at
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *  https://www.apache.org/licenses/LICENSE-2.0.
- * License for the specific language governing permissions and limitations
- * under the License.
  *
- * SPDX-License-Identifier: Apache-2.0
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *  WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *  License for the specific language governing permissions and limitations
+ *  under the License.
+ *
+ *  SPDX-License-Identifier: Apache-2.0
  ******************************************************************************/
 
 package org.deeplearning4j.rl4j.learning.async.nstep.discrete;
 
+import org.deeplearning4j.rl4j.learning.configuration.AsyncQLearningConfiguration;
 import org.deeplearning4j.rl4j.mdp.MDP;
+import org.deeplearning4j.rl4j.network.configuration.DQNDenseNetworkConfiguration;
 import org.deeplearning4j.rl4j.network.dqn.DQNFactory;
 import org.deeplearning4j.rl4j.network.dqn.DQNFactoryStdDense;
 import org.deeplearning4j.rl4j.network.dqn.IDQN;
@@ -32,35 +36,56 @@ public class AsyncNStepQLearningDiscreteDense<O extends Encodable> extends Async
 
     @Deprecated
     public AsyncNStepQLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn,
-                    AsyncNStepQLConfiguration conf, IDataManager dataManager) {
+                                            AsyncNStepQLConfiguration conf, IDataManager dataManager) {
-        super(mdp, dqn, conf);
+        super(mdp, dqn, conf.toLearningConfiguration());
         addListener(new DataManagerTrainingListener(dataManager));
     }
 
+    @Deprecated
     public AsyncNStepQLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn,
                                             AsyncNStepQLConfiguration conf) {
+        super(mdp, dqn, conf.toLearningConfiguration());
+    }
+
+    public AsyncNStepQLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn,
+                                            AsyncQLearningConfiguration conf) {
         super(mdp, dqn, conf);
     }
 
     @Deprecated
     public AsyncNStepQLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, DQNFactory factory,
-                    AsyncNStepQLConfiguration conf, IDataManager dataManager) {
+                                            AsyncNStepQLConfiguration conf, IDataManager dataManager) {
         this(mdp, factory.buildDQN(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf,
-                        dataManager);
+                dataManager);
     }
+
+    @Deprecated
     public AsyncNStepQLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, DQNFactory factory,
                                             AsyncNStepQLConfiguration conf) {
         this(mdp, factory.buildDQN(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf);
     }
 
+    public AsyncNStepQLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, DQNFactory factory,
+                                            AsyncQLearningConfiguration conf) {
+        this(mdp, factory.buildDQN(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf);
+    }
+
     @Deprecated
     public AsyncNStepQLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp,
-                    DQNFactoryStdDense.Configuration netConf, AsyncNStepQLConfiguration conf, IDataManager dataManager) {
+                                            DQNFactoryStdDense.Configuration netConf, AsyncNStepQLConfiguration conf, IDataManager dataManager) {
-        this(mdp, new DQNFactoryStdDense(netConf), conf, dataManager);
+        this(mdp, new DQNFactoryStdDense(netConf.toNetworkConfiguration()), conf, dataManager);
     }
+
+    @Deprecated
     public AsyncNStepQLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp,
                                             DQNFactoryStdDense.Configuration netConf, AsyncNStepQLConfiguration conf) {
+        this(mdp, new DQNFactoryStdDense(netConf.toNetworkConfiguration()), conf);
+    }
+
+    public AsyncNStepQLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp,
+                                            DQNDenseNetworkConfiguration netConf, AsyncQLearningConfiguration conf) {
         this(mdp, new DQNFactoryStdDense(netConf), conf);
     }
 
+
 }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/async/nstep/discrete/AsyncNStepQLearningThreadDiscrete.java

@@ -1,17 +1,18 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
- * This program and the accompanying materials are made available under the
+ *  This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
+ *  terms of the Apache License, Version 2.0 which is available at
- * https://www.apache.org/licenses/LICENSE-2.0.
+ *  https://www.apache.org/licenses/LICENSE-2.0.
  *
- * Unless required by applicable law or agreed to in writing, software
+ *  Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *  distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *  WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations
+ *  License for the specific language governing permissions and limitations
- * under the License.
+ *  under the License.
  *
- * SPDX-License-Identifier: Apache-2.0
+ *  SPDX-License-Identifier: Apache-2.0
  ******************************************************************************/
 
 package org.deeplearning4j.rl4j.learning.async.nstep.discrete;
@@ -22,6 +23,7 @@ import org.deeplearning4j.rl4j.learning.Learning;
 import org.deeplearning4j.rl4j.learning.async.AsyncThreadDiscrete;
 import org.deeplearning4j.rl4j.learning.async.IAsyncGlobal;
 import org.deeplearning4j.rl4j.learning.async.MiniTrans;
+import org.deeplearning4j.rl4j.learning.configuration.AsyncQLearningConfiguration;
 import org.deeplearning4j.rl4j.learning.listener.TrainingListenerList;
 import org.deeplearning4j.rl4j.mdp.MDP;
 import org.deeplearning4j.rl4j.network.dqn.IDQN;
@@ -42,7 +44,7 @@ import java.util.Stack;
 public class AsyncNStepQLearningThreadDiscrete<O extends Encodable> extends AsyncThreadDiscrete<O, IDQN> {
 
     @Getter
-    final protected AsyncNStepQLearningDiscrete.AsyncNStepQLConfiguration conf;
+    final protected AsyncQLearningConfiguration conf;
     @Getter
     final protected IAsyncGlobal<IDQN> asyncGlobal;
     @Getter
@@ -51,7 +53,7 @@ public class AsyncNStepQLearningThreadDiscrete<O extends Encodable> extends Asyn
     final private Random rnd;
 
     public AsyncNStepQLearningThreadDiscrete(MDP<O, Integer, DiscreteSpace> mdp, IAsyncGlobal<IDQN> asyncGlobal,
-                                             AsyncNStepQLearningDiscrete.AsyncNStepQLConfiguration conf,
+                                             AsyncQLearningConfiguration conf,
                                              TrainingListenerList listeners, int threadNumber, int deviceNum) {
         super(asyncGlobal, mdp, listeners, threadNumber, deviceNum);
         this.conf = conf;
@@ -59,7 +61,7 @@ public class AsyncNStepQLearningThreadDiscrete<O extends Encodable> extends Asyn
         this.threadNumber = threadNumber;
         rnd = Nd4j.getRandom();
 
-        Integer seed = conf.getSeed();
+        Long seed = conf.getSeed();
         if(seed != null) {
             rnd.setSeed(seed + threadNumber);
         }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/configuration/A3CLearningConfiguration.java

@@ -0,0 +1,46 @@
+/*******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+package org.deeplearning4j.rl4j.learning.configuration;
+
+import lombok.Builder;
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+import lombok.experimental.SuperBuilder;
+
+@Data
+@SuperBuilder
+@EqualsAndHashCode(callSuper = true)
+public class A3CLearningConfiguration extends LearningConfiguration implements IAsyncLearningConfiguration {
+
+    /**
+     * The number of asynchronous threads to use to generate gradients
+     */
+    private final int numThreads;
+
+    /**
+     * The number of steps to calculate gradients over
+     */
+    private final int nStep;
+
+    /**
+     * The frequency of async training iterations to update the target network.
+     *
+     * If this is set to -1 then the target network is updated after every training iteration
+     */
+    @Builder.Default
+    private int learnerUpdateFrequency = -1;
+}

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/configuration/AsyncQLearningConfiguration.java

@@ -0,0 +1,42 @@
+/*******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+package org.deeplearning4j.rl4j.learning.configuration;
+
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+import lombok.experimental.SuperBuilder;
+
+
+@Data
+@SuperBuilder
+@EqualsAndHashCode(callSuper = true)
+public class AsyncQLearningConfiguration extends QLearningConfiguration implements IAsyncLearningConfiguration {
+
+    /**
+     * The number of asynchronous threads to use to generate experience data
+     */
+    private final int numThreads;
+
+    /**
+     * The number of steps in each training interations
+     */
+    private final int nStep;
+
+    public int getLearnerUpdateFrequency() {
+        return getTargetDqnUpdateFreq();
+    }
+}

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/configuration/IAsyncLearningConfiguration.java

@@ -0,0 +1,28 @@
+/*******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+package org.deeplearning4j.rl4j.learning.configuration;
+
+public interface IAsyncLearningConfiguration extends ILearningConfiguration {
+
+    int getNumThreads();
+
+    int getNStep();
+
+    int getLearnerUpdateFrequency();
+
+    int getMaxStep();
+}

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/configuration/ILearningConfiguration.java

@@ -1,5 +1,5 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -14,36 +14,16 @@
  * SPDX-License-Identifier: Apache-2.0
  ******************************************************************************/
 
-package org.deeplearning4j.rl4j.learning.async;
+package org.deeplearning4j.rl4j.learning.configuration;
 
-import org.deeplearning4j.rl4j.learning.ILearning;
+public interface ILearningConfiguration {
-
+    Long getSeed();
-/**
- * @author rubenfiszel (ruben.fiszel@epfl.ch) on 8/23/16.
- *
- * Interface configuration for all training method that inherit
- * from AsyncLearning
- */
-public interface AsyncConfiguration extends ILearning.LConfiguration {
-
-    Integer getSeed();
 
     int getMaxEpochStep();
 
     int getMaxStep();
 
-    int getNumThread();
-
-    int getNstep();
-
-    int getTargetDqnUpdateFreq();
-
-    int getUpdateStart();
-
-    double getRewardFactor();
-
     double getGamma();
 
-    double getErrorClamp();
+    double getRewardFactor();
-
 }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/configuration/LearningConfiguration.java

@@ -0,0 +1,59 @@
+/*******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+package org.deeplearning4j.rl4j.learning.configuration;
+
+import lombok.Builder;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+import lombok.experimental.SuperBuilder;
+
+@Data
+@SuperBuilder
+@NoArgsConstructor
+public class LearningConfiguration implements ILearningConfiguration {
+
+    /**
+     * Seed value used for training
+     */
+    @Builder.Default
+    private Long seed = System.currentTimeMillis();
+
+    /**
+     * The maximum number of steps in each episode
+     */
+    @Builder.Default
+    private int maxEpochStep = 200;
+
+    /**
+     * The maximum number of steps to train for
+     */
+    @Builder.Default
+    private int maxStep = 150000;
+
+    /**
+     * Gamma parameter used for discounted rewards
+     */
+    @Builder.Default
+    private double gamma = 0.99;
+
+    /**
+     * Scaling parameter for rewards
+     */
+    @Builder.Default
+    private double rewardFactor = 1.0;
+
+}

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/configuration/QLearningConfiguration.java

@@ -0,0 +1,79 @@
+/*******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+package org.deeplearning4j.rl4j.learning.configuration;
+
+import lombok.Builder;
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+import lombok.NoArgsConstructor;
+import lombok.experimental.SuperBuilder;
+
+@Data
+@SuperBuilder
+@NoArgsConstructor
+@EqualsAndHashCode(callSuper = false)
+public class QLearningConfiguration extends LearningConfiguration {
+
+    /**
+     * The maximum size of the experience replay buffer
+     */
+    @Builder.Default
+    private int expRepMaxSize = 150000;
+
+    /**
+     * The batch size of experience for each training iteration
+     */
+    @Builder.Default
+    private int batchSize = 32;
+
+    /**
+     * How many steps between target network updates
+     */
+    @Builder.Default
+    private int targetDqnUpdateFreq = 100;
+
+    /**
+     * The number of steps to initially wait for until samplling batches from experience replay buffer
+     */
+    @Builder.Default
+    private int updateStart = 10;
+
+    /**
+     * Prevent the new Q-Value from being farther than <i>errorClamp</i> away from the previous value. Double.NaN will result in no clamping
+     */
+    @Builder.Default
+    private double errorClamp = 1.0;
+
+    /**
+     * The minimum probability for random exploration action during episilon-greedy annealing
+     */
+    @Builder.Default
+    private double minEpsilon = 0.1f;
+
+    /**
+     * The number of steps to anneal epsilon to its minimum value.
+     */
+    @Builder.Default
+    private int epsilonNbStep = 10000;
+
+    /**
+     * Whether to use the double DQN algorithm
+     */
+    @Builder.Default
+    private boolean doubleDQN = false;
+
+}

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/sync/SyncLearning.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -63,7 +64,7 @@ public abstract class SyncLearning<O, A, AS extends ActionSpace<A>, NN extends N
     /**
      * This method will train the model<p>
      * The training stop when:<br>
-     * - the number of steps reaches the maximum defined in the configuration (see {@link LConfiguration#getMaxStep() LConfiguration.getMaxStep()})<br>
+     * - the number of steps reaches the maximum defined in the configuration (see {@link ILearningConfiguration#getMaxStep() LConfiguration.getMaxStep()})<br>
      * OR<br>
      * - a listener explicitly stops it<br>
      * <p>

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/sync/qlearning/QLearning.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -18,10 +19,19 @@ package org.deeplearning4j.rl4j.learning.sync.qlearning;
 
 import com.fasterxml.jackson.databind.annotation.JsonDeserialize;
 import com.fasterxml.jackson.databind.annotation.JsonPOJOBuilder;
-import lombok.*;
+import lombok.AccessLevel;
+import lombok.AllArgsConstructor;
+import lombok.Builder;
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+import lombok.Getter;
+import lombok.Setter;
+import lombok.Value;
 import lombok.extern.slf4j.Slf4j;
 import org.deeplearning4j.gym.StepReply;
 import org.deeplearning4j.rl4j.learning.EpochStepCounter;
+import org.deeplearning4j.rl4j.learning.configuration.ILearningConfiguration;
+import org.deeplearning4j.rl4j.learning.configuration.QLearningConfiguration;
 import org.deeplearning4j.rl4j.learning.sync.ExpReplay;
 import org.deeplearning4j.rl4j.learning.sync.IExpReplay;
 import org.deeplearning4j.rl4j.learning.sync.SyncLearning;
@@ -59,15 +69,15 @@ public abstract class QLearning<O extends Encodable, A, AS extends ActionSpace<A
 
     protected abstract LegacyMDPWrapper<O, A, AS> getLegacyMDPWrapper();
 
-    public QLearning(QLConfiguration conf) {
+    public QLearning(QLearningConfiguration conf) {
         this(conf, getSeededRandom(conf.getSeed()));
     }
 
-    public QLearning(QLConfiguration conf, Random random) {
+    public QLearning(QLearningConfiguration conf, Random random) {
         expReplay = new ExpReplay<>(conf.getExpRepMaxSize(), conf.getBatchSize(), random);
     }
 
-    private static Random getSeededRandom(Integer seed) {
+    private static Random getSeededRandom(Long seed) {
         Random rnd = Nd4j.getRandom();
         if(seed != null) {
             rnd.setSeed(seed);
@@ -95,7 +105,7 @@ public abstract class QLearning<O extends Encodable, A, AS extends ActionSpace<A
         return getQNetwork();
     }
 
-    public abstract QLConfiguration getConfiguration();
+    public abstract QLearningConfiguration getConfiguration();
 
     protected abstract void preEpoch();
 
@@ -198,7 +208,7 @@ public abstract class QLearning<O extends Encodable, A, AS extends ActionSpace<A
         double reward;
         int episodeLength;
         List<Double> scores;
-        float epsilon;
+        double epsilon;
         double startQ;
         double meanQ;
     }
@@ -213,12 +223,14 @@ public abstract class QLearning<O extends Encodable, A, AS extends ActionSpace<A
 
     }
 
+
     @Data
     @AllArgsConstructor
     @Builder
+    @Deprecated
     @EqualsAndHashCode(callSuper = false)
     @JsonDeserialize(builder = QLConfiguration.QLConfigurationBuilder.class)
-    public static class QLConfiguration implements LConfiguration {
+    public static class QLConfiguration {
 
         Integer seed;
         int maxEpochStep;
@@ -237,7 +249,25 @@ public abstract class QLearning<O extends Encodable, A, AS extends ActionSpace<A
         @JsonPOJOBuilder(withPrefix = "")
         public static final class QLConfigurationBuilder {
         }
+
+        public QLearningConfiguration toLearningConfiguration() {
+
+            return QLearningConfiguration.builder()
+                    .seed(seed.longValue())
+                    .maxEpochStep(maxEpochStep)
+                    .maxStep(maxStep)
+                    .expRepMaxSize(expRepMaxSize)
+                    .batchSize(batchSize)
+                    .targetDqnUpdateFreq(targetDqnUpdateFreq)
+                    .updateStart(updateStart)
+                    .rewardFactor(rewardFactor)
+                    .gamma(gamma)
+                    .errorClamp(errorClamp)
+                    .minEpsilon(minEpsilon)
+                    .epsilonNbStep(epsilonNbStep)
+                    .doubleDQN(doubleDQN)
+                    .build();
+        }
     }
 
-
 }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/sync/qlearning/discrete/QLearningDiscrete.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -22,6 +23,7 @@ import lombok.Setter;
 import org.deeplearning4j.gym.StepReply;
 import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
 import org.deeplearning4j.rl4j.learning.Learning;
+import org.deeplearning4j.rl4j.learning.configuration.QLearningConfiguration;
 import org.deeplearning4j.rl4j.learning.sync.Transition;
 import org.deeplearning4j.rl4j.learning.sync.qlearning.QLearning;
 import org.deeplearning4j.rl4j.learning.sync.qlearning.discrete.TDTargetAlgorithm.DoubleDQN;
@@ -45,16 +47,15 @@ import java.util.ArrayList;
 
 /**
  * @author rubenfiszel (ruben.fiszel@epfl.ch) 7/18/16.
- *
+ * <p>
  * DQN or Deep Q-Learning in the Discrete domain
- *
+ * <p>
  * http://arxiv.org/abs/1312.5602
- *
  */
 public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O, Integer, DiscreteSpace> {
 
     @Getter
-    final private QLConfiguration configuration;
+    final private QLearningConfiguration configuration;
     private final LegacyMDPWrapper<O, Integer, DiscreteSpace> mdp;
     @Getter
     private DQNPolicy<O> policy;
@@ -78,16 +79,15 @@ public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O
         return mdp;
     }
 
-    public QLearningDiscrete(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, QLConfiguration conf,
+    public QLearningDiscrete(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, QLearningConfiguration conf, int epsilonNbStep) {
-                             int epsilonNbStep) {
         this(mdp, dqn, conf, epsilonNbStep, Nd4j.getRandomFactory().getNewRandomInstance(conf.getSeed()));
     }
 
-    public QLearningDiscrete(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, QLConfiguration conf,
+    public QLearningDiscrete(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, QLearningConfiguration conf,
                              int epsilonNbStep, Random random) {
         super(conf);
         this.configuration = conf;
-        this.mdp = new LegacyMDPWrapper<O, Integer, DiscreteSpace>(mdp, null, this);
+        this.mdp = new LegacyMDPWrapper<>(mdp, null, this);
         qNetwork = dqn;
         targetQNetwork = dqn.clone();
         policy = new DQNPolicy(getQNetwork());
@@ -125,6 +125,7 @@ public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O
 
     /**
      * Single step of training
+     *
      * @param obs last obs
      * @return relevant info for next step
      */
@@ -135,8 +136,8 @@ public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O
         boolean isHistoryProcessor = getHistoryProcessor() != null;
         int skipFrame = isHistoryProcessor ? getHistoryProcessor().getConf().getSkipFrame() : 1;
         int historyLength = isHistoryProcessor ? getHistoryProcessor().getConf().getHistoryLength() : 1;
-        int updateStart = getConfiguration().getUpdateStart()
+        int updateStart = this.getConfiguration().getUpdateStart()
-                        + ((getConfiguration().getBatchSize() + historyLength) * skipFrame);
+                + ((this.getConfiguration().getBatchSize() + historyLength) * skipFrame);
 
         Double maxQ = Double.NaN; //ignore if Nan for stats
 
@@ -161,7 +162,7 @@ public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O
         if (!obs.isSkipped()) {
 
             // Add experience
-            if(pendingTransition != null) {
+            if (pendingTransition != null) {
                 pendingTransition.setNextObservation(obs);
                 getExpReplay().store(pendingTransition);
             }
@@ -188,7 +189,7 @@ public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O
 
     @Override
     protected void finishEpoch(Observation observation) {
-        if(pendingTransition != null) {
+        if (pendingTransition != null) {
             pendingTransition.setNextObservation(observation);
             getExpReplay().store(pendingTransition);
         }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/sync/qlearning/discrete/QLearningDiscreteConv.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -17,7 +18,9 @@
 package org.deeplearning4j.rl4j.learning.sync.qlearning.discrete;
 
 import org.deeplearning4j.rl4j.learning.HistoryProcessor;
+import org.deeplearning4j.rl4j.learning.configuration.QLearningConfiguration;
 import org.deeplearning4j.rl4j.mdp.MDP;
+import org.deeplearning4j.rl4j.network.configuration.NetworkConfiguration;
 import org.deeplearning4j.rl4j.network.dqn.DQNFactory;
 import org.deeplearning4j.rl4j.network.dqn.DQNFactoryStdConv;
 import org.deeplearning4j.rl4j.network.dqn.IDQN;
@@ -36,33 +39,55 @@ public class QLearningDiscreteConv<O extends Encodable> extends QLearningDiscret
 
     @Deprecated
     public QLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, HistoryProcessor.Configuration hpconf,
-                    QLConfiguration conf, IDataManager dataManager) {
+                                 QLConfiguration conf, IDataManager dataManager) {
         this(mdp, dqn, hpconf, conf);
         addListener(new DataManagerTrainingListener(dataManager));
     }
+
+    @Deprecated
     public QLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, HistoryProcessor.Configuration hpconf,
                                  QLConfiguration conf) {
+        super(mdp, dqn, conf.toLearningConfiguration(), conf.getEpsilonNbStep() * hpconf.getSkipFrame());
+        setHistoryProcessor(hpconf);
+    }
+
+    public QLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, HistoryProcessor.Configuration hpconf,
+                                 QLearningConfiguration conf) {
         super(mdp, dqn, conf, conf.getEpsilonNbStep() * hpconf.getSkipFrame());
         setHistoryProcessor(hpconf);
     }
 
     @Deprecated
     public QLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, DQNFactory factory,
-                    HistoryProcessor.Configuration hpconf, QLConfiguration conf, IDataManager dataManager) {
+                                 HistoryProcessor.Configuration hpconf, QLConfiguration conf, IDataManager dataManager) {
         this(mdp, factory.buildDQN(hpconf.getShape(), mdp.getActionSpace().getSize()), hpconf, conf, dataManager);
     }
+
+    @Deprecated
     public QLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, DQNFactory factory,
                                  HistoryProcessor.Configuration hpconf, QLConfiguration conf) {
         this(mdp, factory.buildDQN(hpconf.getShape(), mdp.getActionSpace().getSize()), hpconf, conf);
     }
 
+    public QLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, DQNFactory factory,
+                                 HistoryProcessor.Configuration hpconf, QLearningConfiguration conf) {
+        this(mdp, factory.buildDQN(hpconf.getShape(), mdp.getActionSpace().getSize()), hpconf, conf);
+    }
+
     @Deprecated
     public QLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, DQNFactoryStdConv.Configuration netConf,
-                    HistoryProcessor.Configuration hpconf, QLConfiguration conf, IDataManager dataManager) {
+                                 HistoryProcessor.Configuration hpconf, QLConfiguration conf, IDataManager dataManager) {
-        this(mdp, new DQNFactoryStdConv(netConf), hpconf, conf, dataManager);
+        this(mdp, new DQNFactoryStdConv(netConf.toNetworkConfiguration()), hpconf, conf, dataManager);
     }
+
+    @Deprecated
     public QLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, DQNFactoryStdConv.Configuration netConf,
                                  HistoryProcessor.Configuration hpconf, QLConfiguration conf) {
+        this(mdp, new DQNFactoryStdConv(netConf.toNetworkConfiguration()), hpconf, conf);
+    }
+
+    public QLearningDiscreteConv(MDP<O, Integer, DiscreteSpace> mdp, NetworkConfiguration netConf,
+                                 HistoryProcessor.Configuration hpconf, QLearningConfiguration conf) {
         this(mdp, new DQNFactoryStdConv(netConf), hpconf, conf);
     }
 

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/learning/sync/qlearning/discrete/QLearningDiscreteDense.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -16,8 +17,10 @@
 
 package org.deeplearning4j.rl4j.learning.sync.qlearning.discrete;
 
+import org.deeplearning4j.rl4j.learning.configuration.QLearningConfiguration;
 import org.deeplearning4j.rl4j.learning.sync.qlearning.QLearning;
 import org.deeplearning4j.rl4j.mdp.MDP;
+import org.deeplearning4j.rl4j.network.configuration.DQNDenseNetworkConfiguration;
 import org.deeplearning4j.rl4j.network.dqn.DQNFactory;
 import org.deeplearning4j.rl4j.network.dqn.DQNFactoryStdDense;
 import org.deeplearning4j.rl4j.network.dqn.IDQN;
@@ -38,7 +41,13 @@ public class QLearningDiscreteDense<O extends Encodable> extends QLearningDiscre
         this(mdp, dqn, conf);
         addListener(new DataManagerTrainingListener(dataManager));
     }
+
+    @Deprecated
     public QLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, QLearning.QLConfiguration conf) {
+        super(mdp, dqn, conf.toLearningConfiguration(), conf.getEpsilonNbStep());
+    }
+
+    public QLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, QLearningConfiguration conf) {
         super(mdp, dqn, conf, conf.getEpsilonNbStep());
     }
 
@@ -48,18 +57,33 @@ public class QLearningDiscreteDense<O extends Encodable> extends QLearningDiscre
         this(mdp, factory.buildDQN(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf,
                         dataManager);
     }
+
+    @Deprecated
     public QLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, DQNFactory factory,
                                   QLearning.QLConfiguration conf) {
         this(mdp, factory.buildDQN(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf);
     }
 
+    public QLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, DQNFactory factory,
+                                  QLearningConfiguration conf) {
+        this(mdp, factory.buildDQN(mdp.getObservationSpace().getShape(), mdp.getActionSpace().getSize()), conf);
+    }
+
     @Deprecated
     public QLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, DQNFactoryStdDense.Configuration netConf,
                     QLearning.QLConfiguration conf, IDataManager dataManager) {
-        this(mdp, new DQNFactoryStdDense(netConf), conf, dataManager);
+
+        this(mdp, new DQNFactoryStdDense(netConf.toNetworkConfiguration()), conf, dataManager);
     }
+
+    @Deprecated
     public QLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, DQNFactoryStdDense.Configuration netConf,
                                   QLearning.QLConfiguration conf) {
+        this(mdp, new DQNFactoryStdDense(netConf.toNetworkConfiguration()), conf);
+    }
+
+    public QLearningDiscreteDense(MDP<O, Integer, DiscreteSpace> mdp, DQNDenseNetworkConfiguration netConf,
+                                  QLearningConfiguration conf) {
         this(mdp, new DQNFactoryStdDense(netConf), conf);
     }
 

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/ac/ActorCriticCompGraph.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -36,7 +37,7 @@ import java.util.Collection;
  *
  * Standard implementation of ActorCriticCompGraph
  */
-public class ActorCriticCompGraph<NN extends ActorCriticCompGraph> implements IActorCritic<NN> {
+public class ActorCriticCompGraph implements IActorCritic<ActorCriticCompGraph> {
 
     final protected ComputationGraph cg;
     @Getter
@@ -73,13 +74,13 @@ public class ActorCriticCompGraph<NN extends ActorCriticCompGraph> implements IA
         }
     }
 
-    public NN clone() {
+    public ActorCriticCompGraph clone() {
-        NN nn = (NN)new ActorCriticCompGraph(cg.clone());
+        ActorCriticCompGraph nn = new ActorCriticCompGraph(cg.clone());
         nn.cg.setListeners(cg.getListeners());
         return nn;
     }
 
-    public void copy(NN from) {
+    public void copy(ActorCriticCompGraph from) {
         cg.setParams(from.cg.params());
     }
 

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/ac/ActorCriticFactoryCompGraphStdConv.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -31,12 +32,16 @@ import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.weights.WeightInit;
 import org.deeplearning4j.optimize.api.TrainingListener;
 import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
+import org.deeplearning4j.rl4j.network.configuration.ActorCriticNetworkConfiguration;
+import org.deeplearning4j.rl4j.network.configuration.ActorCriticNetworkConfiguration.ActorCriticNetworkConfigurationBuilder;
 import org.deeplearning4j.rl4j.util.Constants;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.learning.config.Adam;
 import org.nd4j.linalg.learning.config.IUpdater;
 import org.nd4j.linalg.lossfunctions.LossFunctions;
 
+import java.util.Arrays;
+
 /**
  * @author rubenfiszel (ruben.fiszel@epfl.ch) on 8/9/16.
  *
@@ -45,8 +50,7 @@ import org.nd4j.linalg.lossfunctions.LossFunctions;
 @Value
 public class ActorCriticFactoryCompGraphStdConv implements ActorCriticFactoryCompGraph {
 
-
+    ActorCriticNetworkConfiguration conf;
-    Configuration conf;
 
     public ActorCriticCompGraph buildActorCritic(int shapeInputs[], int numOutputs) {
 
@@ -109,16 +113,33 @@ public class ActorCriticFactoryCompGraphStdConv implements ActorCriticFactoryCom
         return new ActorCriticCompGraph(model);
     }
 
-
     @AllArgsConstructor
     @Builder
     @Value
+    @Deprecated
     public static class Configuration {
 
         double l2;
         IUpdater updater;
         TrainingListener[] listeners;
         boolean useLSTM;
+
+        /**
+         * Converts the deprecated Configuration to the new NetworkConfiguration format
+         */
+        public ActorCriticNetworkConfiguration toNetworkConfiguration() {
+            ActorCriticNetworkConfigurationBuilder builder = ActorCriticNetworkConfiguration.builder()
+                    .l2(l2)
+                    .updater(updater)
+                    .useLSTM(useLSTM);
+
+            if (listeners != null) {
+                builder.listeners(Arrays.asList(listeners));
+            }
+
+            return builder.build();
+
+        }
     }
 
 }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/ac/ActorCriticFactoryCompGraphStdDense.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -16,8 +17,6 @@
 
 package org.deeplearning4j.rl4j.network.ac;
 
-import lombok.AllArgsConstructor;
-import lombok.Builder;
 import lombok.Value;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
@@ -29,12 +28,11 @@ import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.weights.WeightInit;
-import org.deeplearning4j.optimize.api.TrainingListener;
 import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
+import org.deeplearning4j.rl4j.network.configuration.ActorCriticDenseNetworkConfiguration;
 import org.deeplearning4j.rl4j.util.Constants;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.learning.config.Adam;
-import org.nd4j.linalg.learning.config.IUpdater;
 import org.nd4j.linalg.lossfunctions.LossFunctions;
 
 /**
@@ -45,7 +43,7 @@ import org.nd4j.linalg.lossfunctions.LossFunctions;
 @Value
 public class ActorCriticFactoryCompGraphStdDense implements ActorCriticFactoryCompGraph {
 
-    Configuration conf;
+    ActorCriticDenseNetworkConfiguration conf;
 
     public ActorCriticCompGraph buildActorCritic(int[] numInputs, int numOutputs) {
         int nIn = 1;
@@ -65,27 +63,27 @@ public class ActorCriticFactoryCompGraphStdDense implements ActorCriticFactoryCo
                                                         "input");
 
 
-        for (int i = 1; i < conf.getNumLayer(); i++) {
+        for (int i = 1; i < conf.getNumLayers(); i++) {
             confB.addLayer(i + "", new DenseLayer.Builder().nIn(conf.getNumHiddenNodes()).nOut(conf.getNumHiddenNodes())
                             .activation(Activation.RELU).build(), (i - 1) + "");
         }
 
 
         if (conf.isUseLSTM()) {
-            confB.addLayer(getConf().getNumLayer() + "", new LSTM.Builder().activation(Activation.TANH)
+            confB.addLayer(getConf().getNumLayers() + "", new LSTM.Builder().activation(Activation.TANH)
-                            .nOut(conf.getNumHiddenNodes()).build(), (getConf().getNumLayer() - 1) + "");
+                            .nOut(conf.getNumHiddenNodes()).build(), (getConf().getNumLayers() - 1) + "");
 
             confB.addLayer("value", new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
-                            .nOut(1).build(), getConf().getNumLayer() + "");
+                            .nOut(1).build(), getConf().getNumLayers() + "");
 
             confB.addLayer("softmax", new RnnOutputLayer.Builder(new ActorCriticLoss()).activation(Activation.SOFTMAX)
-                            .nOut(numOutputs).build(), getConf().getNumLayer() + "");
+                            .nOut(numOutputs).build(), getConf().getNumLayers() + "");
         } else {
             confB.addLayer("value", new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
-                            .nOut(1).build(), (getConf().getNumLayer() - 1) + "");
+                            .nOut(1).build(), (getConf().getNumLayers() - 1) + "");
 
             confB.addLayer("softmax", new OutputLayer.Builder(new ActorCriticLoss()).activation(Activation.SOFTMAX)
-                            .nOut(numOutputs).build(), (getConf().getNumLayer() - 1) + "");
+                            .nOut(numOutputs).build(), (getConf().getNumLayers() - 1) + "");
         }
 
         confB.setOutputs("value", "softmax");
@@ -103,18 +101,4 @@ public class ActorCriticFactoryCompGraphStdDense implements ActorCriticFactoryCo
         return new ActorCriticCompGraph(model);
     }
 
-    @AllArgsConstructor
-    @Builder
-    @Value
-    public static class Configuration {
-
-        int numLayer;
-        int numHiddenNodes;
-        double l2;
-        IUpdater updater;
-        TrainingListener[] listeners;
-        boolean useLSTM;
-    }
-
-
 }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/ac/ActorCriticFactorySeparateStdDense.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -31,21 +32,24 @@ import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
 import org.deeplearning4j.optimize.api.TrainingListener;
 import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
+
+import org.deeplearning4j.rl4j.network.configuration.ActorCriticDenseNetworkConfiguration;
+import org.deeplearning4j.rl4j.network.configuration.ActorCriticDenseNetworkConfiguration.ActorCriticDenseNetworkConfigurationBuilder;
 import org.deeplearning4j.rl4j.util.Constants;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.learning.config.Adam;
 import org.nd4j.linalg.learning.config.IUpdater;
 import org.nd4j.linalg.lossfunctions.LossFunctions;
 
+import java.util.Arrays;
+
 /**
  * @author rubenfiszel (ruben.fiszel@epfl.ch) on 8/9/16.
- *
- *
  */
 @Value
 public class ActorCriticFactorySeparateStdDense implements ActorCriticFactorySeparate {
 
-    Configuration conf;
+    ActorCriticDenseNetworkConfiguration conf;
 
     public ActorCriticSeparate buildActorCritic(int[] numInputs, int numOutputs) {
         int nIn = 1;
@@ -53,27 +57,27 @@ public class ActorCriticFactorySeparateStdDense implements ActorCriticFactorySep
             nIn *= i;
         }
         NeuralNetConfiguration.ListBuilder confB = new NeuralNetConfiguration.Builder().seed(Constants.NEURAL_NET_SEED)
-                        .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
+                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
-                        .updater(conf.getUpdater() != null ? conf.getUpdater() : new Adam())
+                .updater(conf.getUpdater() != null ? conf.getUpdater() : new Adam())
-                        .weightInit(WeightInit.XAVIER)
+                .weightInit(WeightInit.XAVIER)
-                        .l2(conf.getL2())
+                .l2(conf.getL2())
-                        .list().layer(0, new DenseLayer.Builder().nIn(nIn).nOut(conf.getNumHiddenNodes())
+                .list().layer(0, new DenseLayer.Builder().nIn(nIn).nOut(conf.getNumHiddenNodes())
-                                        .activation(Activation.RELU).build());
+                        .activation(Activation.RELU).build());
 
 
-        for (int i = 1; i < conf.getNumLayer(); i++) {
+        for (int i = 1; i < conf.getNumLayers(); i++) {
             confB.layer(i, new DenseLayer.Builder().nIn(conf.getNumHiddenNodes()).nOut(conf.getNumHiddenNodes())
-                            .activation(Activation.RELU).build());
+                    .activation(Activation.RELU).build());
         }
 
         if (conf.isUseLSTM()) {
-            confB.layer(conf.getNumLayer(), new LSTM.Builder().nOut(conf.getNumHiddenNodes()).activation(Activation.TANH).build());
+            confB.layer(conf.getNumLayers(), new LSTM.Builder().nOut(conf.getNumHiddenNodes()).activation(Activation.TANH).build());
 
-            confB.layer(conf.getNumLayer() + 1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
+            confB.layer(conf.getNumLayers() + 1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
-                            .nIn(conf.getNumHiddenNodes()).nOut(1).build());
+                    .nIn(conf.getNumHiddenNodes()).nOut(1).build());
         } else {
-            confB.layer(conf.getNumLayer(), new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
+            confB.layer(conf.getNumLayers(), new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
-                            .nIn(conf.getNumHiddenNodes()).nOut(1).build());
+                    .nIn(conf.getNumHiddenNodes()).nOut(1).build());
         }
 
         confB.setInputType(conf.isUseLSTM() ? InputType.recurrent(nIn) : InputType.feedForward(nIn));
@@ -87,28 +91,28 @@ public class ActorCriticFactorySeparateStdDense implements ActorCriticFactorySep
         }
 
         NeuralNetConfiguration.ListBuilder confB2 = new NeuralNetConfiguration.Builder().seed(Constants.NEURAL_NET_SEED)
-                        .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
+                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
-                        .updater(conf.getUpdater() != null ? conf.getUpdater() : new Adam())
+                .updater(conf.getUpdater() != null ? conf.getUpdater() : new Adam())
-                        .weightInit(WeightInit.XAVIER)
+                .weightInit(WeightInit.XAVIER)
-                        //.regularization(true)
+                //.regularization(true)
-                        //.l2(conf.getL2())
+                //.l2(conf.getL2())
-                        .list().layer(0, new DenseLayer.Builder().nIn(nIn).nOut(conf.getNumHiddenNodes())
+                .list().layer(0, new DenseLayer.Builder().nIn(nIn).nOut(conf.getNumHiddenNodes())
-                                        .activation(Activation.RELU).build());
+                        .activation(Activation.RELU).build());
 
 
-        for (int i = 1; i < conf.getNumLayer(); i++) {
+        for (int i = 1; i < conf.getNumLayers(); i++) {
             confB2.layer(i, new DenseLayer.Builder().nIn(conf.getNumHiddenNodes()).nOut(conf.getNumHiddenNodes())
-                            .activation(Activation.RELU).build());
+                    .activation(Activation.RELU).build());
         }
 
         if (conf.isUseLSTM()) {
-            confB2.layer(conf.getNumLayer(), new LSTM.Builder().nOut(conf.getNumHiddenNodes()).activation(Activation.TANH).build());
+            confB2.layer(conf.getNumLayers(), new LSTM.Builder().nOut(conf.getNumHiddenNodes()).activation(Activation.TANH).build());
 
-            confB2.layer(conf.getNumLayer() + 1, new RnnOutputLayer.Builder(new ActorCriticLoss())
+            confB2.layer(conf.getNumLayers() + 1, new RnnOutputLayer.Builder(new ActorCriticLoss())
-                            .activation(Activation.SOFTMAX).nIn(conf.getNumHiddenNodes()).nOut(numOutputs).build());
+                    .activation(Activation.SOFTMAX).nIn(conf.getNumHiddenNodes()).nOut(numOutputs).build());
         } else {
-            confB2.layer(conf.getNumLayer(), new OutputLayer.Builder(new ActorCriticLoss())
+            confB2.layer(conf.getNumLayers(), new OutputLayer.Builder(new ActorCriticLoss())
-                            .activation(Activation.SOFTMAX).nIn(conf.getNumHiddenNodes()).nOut(numOutputs).build());
+                    .activation(Activation.SOFTMAX).nIn(conf.getNumHiddenNodes()).nOut(numOutputs).build());
         }
 
         confB2.setInputType(conf.isUseLSTM() ? InputType.recurrent(nIn) : InputType.feedForward(nIn));
@@ -128,6 +132,7 @@ public class ActorCriticFactorySeparateStdDense implements ActorCriticFactorySep
     @AllArgsConstructor
     @Value
     @Builder
+    @Deprecated
     public static class Configuration {
 
         int numLayer;
@@ -136,6 +141,22 @@ public class ActorCriticFactorySeparateStdDense implements ActorCriticFactorySep
         IUpdater updater;
         TrainingListener[] listeners;
         boolean useLSTM;
+
+        public ActorCriticDenseNetworkConfiguration toNetworkConfiguration() {
+            ActorCriticDenseNetworkConfigurationBuilder builder = ActorCriticDenseNetworkConfiguration.builder()
+                    .numHiddenNodes(numHiddenNodes)
+                    .numLayers(numLayer)
+                    .l2(l2)
+                    .updater(updater)
+                    .useLSTM(useLSTM);
+
+            if (listeners != null) {
+                builder.listeners(Arrays.asList(listeners));
+            }
+
+            return builder.build();
+
+        }
     }
 
 

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/configuration/ActorCriticDenseNetworkConfiguration.java

@@ -0,0 +1,42 @@
+/*******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+package org.deeplearning4j.rl4j.network.configuration;
+
+import lombok.Builder;
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+import lombok.experimental.SuperBuilder;
+
+@Data
+@SuperBuilder
+@EqualsAndHashCode(callSuper = true)
+public class ActorCriticDenseNetworkConfiguration extends ActorCriticNetworkConfiguration {
+
+    /**
+     * The number of layers in the dense network
+     */
+    @Builder.Default
+    private int numLayers = 3;
+
+    /**
+     * The number of hidden neurons in each layer
+     */
+    @Builder.Default
+    private int numHiddenNodes = 100;
+
+
+}

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/configuration/ActorCriticNetworkConfiguration.java

@@ -0,0 +1,37 @@
+/*******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+package org.deeplearning4j.rl4j.network.configuration;
+
+import lombok.Builder;
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+import lombok.NoArgsConstructor;
+import lombok.experimental.SuperBuilder;
+
+@Data
+@SuperBuilder
+@NoArgsConstructor
+@EqualsAndHashCode(callSuper = true)
+public class ActorCriticNetworkConfiguration extends NetworkConfiguration {
+
+    /**
+     * Whether or not to add an LSTM layer to the network.
+     */
+    @Builder.Default
+    private boolean useLSTM = false;
+
+}

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/configuration/DQNDenseNetworkConfiguration.java

@@ -0,0 +1,40 @@
+/*******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+package org.deeplearning4j.rl4j.network.configuration;
+
+import lombok.Builder;
+import lombok.Data;
+import lombok.EqualsAndHashCode;
+import lombok.experimental.SuperBuilder;
+
+@Data
+@SuperBuilder
+@EqualsAndHashCode(callSuper = true)
+public class DQNDenseNetworkConfiguration extends NetworkConfiguration {
+
+    /**
+     * The number of layers in the dense network
+     */
+    @Builder.Default
+    private int numLayers = 3;
+
+    /**
+     * The number of hidden neurons in each layer
+     */
+    @Builder.Default
+    private int numHiddenNodes = 100;
+}

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/configuration/NetworkConfiguration.java

@@ -0,0 +1,58 @@
+/*******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
+package org.deeplearning4j.rl4j.network.configuration;
+
+import lombok.Builder;
+import lombok.Data;
+import lombok.NoArgsConstructor;
+import lombok.Singular;
+import lombok.experimental.SuperBuilder;
+import org.deeplearning4j.optimize.api.TrainingListener;
+import org.nd4j.linalg.learning.config.IUpdater;
+
+import java.util.List;
+
+
+@Data
+@SuperBuilder
+@NoArgsConstructor
+public class NetworkConfiguration {
+
+    /**
+     * The learning rate of the network
+     */
+    @Builder.Default
+    private double learningRate = 0.01;
+
+    /**
+     * L2 regularization on the network
+     */
+    @Builder.Default
+    private double l2 = 0.0;
+
+    /**
+     * The network's gradient update algorithm
+     */
+    private IUpdater updater;
+
+    /**
+     * Training listeners attached to the network
+     */
+    @Singular
+    private List<TrainingListener> listeners;
+
+}

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/dqn/DQNFactoryStdConv.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -30,12 +31,15 @@ import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
 import org.deeplearning4j.optimize.api.TrainingListener;
 import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
+import org.deeplearning4j.rl4j.network.configuration.NetworkConfiguration;
 import org.deeplearning4j.rl4j.util.Constants;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.learning.config.Adam;
 import org.nd4j.linalg.learning.config.IUpdater;
 import org.nd4j.linalg.lossfunctions.LossFunctions;
 
+import java.util.Arrays;
+
 /**
  * @author rubenfiszel (ruben.fiszel@epfl.ch) 7/13/16.
  */
@@ -43,7 +47,7 @@ import org.nd4j.linalg.lossfunctions.LossFunctions;
 public class DQNFactoryStdConv implements DQNFactory {
 
 
-    Configuration conf;
+    NetworkConfiguration conf;
 
     public DQN buildDQN(int shapeInputs[], int numOutputs) {
 
@@ -80,7 +84,6 @@ public class DQNFactoryStdConv implements DQNFactory {
         return new DQN(model);
     }
 
-
     @AllArgsConstructor
     @Builder
     @Value
@@ -90,6 +93,23 @@ public class DQNFactoryStdConv implements DQNFactory {
         double l2;
         IUpdater updater;
         TrainingListener[] listeners;
+
+        /**
+         * Converts the deprecated Configuration to the new NetworkConfiguration format
+         */
+        public NetworkConfiguration toNetworkConfiguration() {
+            NetworkConfiguration.NetworkConfigurationBuilder builder = NetworkConfiguration.builder()
+                    .learningRate(learningRate)
+                    .l2(l2)
+                    .updater(updater);
+
+            if (listeners != null) {
+                builder.listeners(Arrays.asList(listeners));
+            }
+
+            return builder.build();
+
+        }
     }
 
 }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/network/dqn/DQNFactoryStdDense.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -28,12 +29,16 @@ import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
 import org.deeplearning4j.optimize.api.TrainingListener;
 import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
+import org.deeplearning4j.rl4j.network.configuration.DQNDenseNetworkConfiguration;
+import org.deeplearning4j.rl4j.network.configuration.DQNDenseNetworkConfiguration.DQNDenseNetworkConfigurationBuilder;
 import org.deeplearning4j.rl4j.util.Constants;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.learning.config.Adam;
 import org.nd4j.linalg.learning.config.IUpdater;
 import org.nd4j.linalg.lossfunctions.LossFunctions;
 
+import java.util.Arrays;
+
 /**
  * @author rubenfiszel (ruben.fiszel@epfl.ch) 7/13/16.
  */
@@ -41,32 +46,41 @@ import org.nd4j.linalg.lossfunctions.LossFunctions;
 @Value
 public class DQNFactoryStdDense implements DQNFactory {
 
-
+    DQNDenseNetworkConfiguration conf;
-    Configuration conf;
 
     public DQN buildDQN(int[] numInputs, int numOutputs) {
         int nIn = 1;
+
         for (int i : numInputs) {
             nIn *= i;
         }
+
         NeuralNetConfiguration.ListBuilder confB = new NeuralNetConfiguration.Builder().seed(Constants.NEURAL_NET_SEED)
-                        .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
+                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
-                        //.updater(Updater.NESTEROVS).momentum(0.9)
+                .updater(conf.getUpdater() != null ? conf.getUpdater() : new Adam())
-                        //.updater(Updater.RMSPROP).rho(conf.getRmsDecay())//.rmsDecay(conf.getRmsDecay())
+                .weightInit(WeightInit.XAVIER)
-                        .updater(conf.getUpdater() != null ? conf.getUpdater() : new Adam())
+                .l2(conf.getL2())
-                        .weightInit(WeightInit.XAVIER)
+                .list()
-                        .l2(conf.getL2())
+                .layer(0,
-                        .list().layer(0, new DenseLayer.Builder().nIn(nIn).nOut(conf.getNumHiddenNodes())
+                        new DenseLayer.Builder()
-                                        .activation(Activation.RELU).build());
+                                .nIn(nIn)
+                                .nOut(conf.getNumHiddenNodes())
+                                .activation(Activation.RELU).build()
+                );
 
 
-        for (int i = 1; i < conf.getNumLayer(); i++) {
+        for (int i = 1; i < conf.getNumLayers(); i++) {
             confB.layer(i, new DenseLayer.Builder().nIn(conf.getNumHiddenNodes()).nOut(conf.getNumHiddenNodes())
-                            .activation(Activation.RELU).build());
+                    .activation(Activation.RELU).build());
         }
 
-        confB.layer(conf.getNumLayer(), new OutputLayer.Builder(LossFunctions.LossFunction.MSE).activation(Activation.IDENTITY)
+        confB.layer(conf.getNumLayers(),
-                        .nIn(conf.getNumHiddenNodes()).nOut(numOutputs).build());
+                new OutputLayer.Builder(LossFunctions.LossFunction.MSE)
+                        .activation(Activation.IDENTITY)
+                        .nIn(conf.getNumHiddenNodes())
+                        .nOut(numOutputs)
+                        .build()
+        );
 
 
         MultiLayerConfiguration mlnconf = confB.build();
@@ -83,6 +97,7 @@ public class DQNFactoryStdDense implements DQNFactory {
     @AllArgsConstructor
     @Value
     @Builder
+    @Deprecated
     public static class Configuration {
 
         int numLayer;
@@ -90,7 +105,23 @@ public class DQNFactoryStdDense implements DQNFactory {
         double l2;
         IUpdater updater;
         TrainingListener[] listeners;
+
+        /**
+         * Converts the deprecated Configuration to the new NetworkConfiguration format
+         */
+        public DQNDenseNetworkConfiguration toNetworkConfiguration() {
+            DQNDenseNetworkConfigurationBuilder builder = DQNDenseNetworkConfiguration.builder()
+                    .numHiddenNodes(numHiddenNodes)
+                    .numLayers(numLayer)
+                    .l2(l2)
+                    .updater(updater);
+
+            if (listeners != null) {
+                builder.listeners(Arrays.asList(listeners));
+            }
+
+            return builder.build();
+        }
     }
 
-
 }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/policy/EpsGreedy.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -46,7 +47,7 @@ public class EpsGreedy<O, A, AS extends ActionSpace<A>> extends Policy<O, A> {
     final private int updateStart;
     final private int epsilonNbStep;
     final private Random rnd;
-    final private float minEpsilon;
+    final private double minEpsilon;
     final private IEpochTrainer learning;
 
     public NeuralNet getNeuralNet() {
@@ -55,10 +56,10 @@ public class EpsGreedy<O, A, AS extends ActionSpace<A>> extends Policy<O, A> {
 
     public A nextAction(INDArray input) {
 
-        float ep = getEpsilon();
+        double ep = getEpsilon();
         if (learning.getStepCounter() % 500 == 1)
             log.info("EP: " + ep + " " + learning.getStepCounter());
-        if (rnd.nextFloat() > ep)
+        if (rnd.nextDouble() > ep)
             return policy.nextAction(input);
         else
             return mdp.getActionSpace().randomAction();
@@ -68,7 +69,7 @@ public class EpsGreedy<O, A, AS extends ActionSpace<A>> extends Policy<O, A> {
         return this.nextAction(observation.getData());
     }
 
-    public float getEpsilon() {
+    public double getEpsilon() {
-        return Math.min(1f, Math.max(minEpsilon, 1f - (learning.getStepCounter() - updateStart) * 1f / epsilonNbStep));
+        return Math.min(1.0, Math.max(minEpsilon, 1.0 - (learning.getStepCounter() - updateStart) * 1.0 / epsilonNbStep));
     }
 }

rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/util/DataManager.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -22,17 +23,30 @@ import lombok.Builder;
 import lombok.Getter;
 import lombok.Value;
 import lombok.extern.slf4j.Slf4j;
-import org.deeplearning4j.rl4j.learning.NeuralNetFetchable;
-import org.nd4j.linalg.primitives.Pair;
 import org.deeplearning4j.rl4j.learning.ILearning;
-import org.deeplearning4j.rl4j.learning.Learning;
+import org.deeplearning4j.rl4j.learning.NeuralNetFetchable;
+import org.deeplearning4j.rl4j.learning.configuration.ILearningConfiguration;
 import org.deeplearning4j.rl4j.network.ac.IActorCritic;
 import org.deeplearning4j.rl4j.network.dqn.DQN;
 import org.deeplearning4j.rl4j.network.dqn.IDQN;
 import org.deeplearning4j.util.ModelSerializer;
+import org.nd4j.linalg.primitives.Pair;
 
-import java.io.*;
+import java.io.BufferedOutputStream;
-import java.nio.file.*;
+import java.io.BufferedReader;
+import java.io.ByteArrayInputStream;
+import java.io.ByteArrayOutputStream;
+import java.io.File;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+import java.io.OutputStream;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+import java.nio.file.StandardCopyOption;
+import java.nio.file.StandardOpenOption;
 import java.util.zip.ZipEntry;
 import java.util.zip.ZipFile;
 import java.util.zip.ZipOutputStream;
@@ -304,7 +318,7 @@ public class DataManager implements IDataManager {
     public static class Info {
         String trainingName;
         String mdpName;
-        ILearning.LConfiguration conf;
+        ILearningConfiguration conf;
         int stepCounter;
         long millisTime;
     }

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/learning/HistoryProcessorTest.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
  * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -16,14 +17,11 @@
 
 package org.deeplearning4j.rl4j.learning;
 
-import java.util.Arrays;
 import org.junit.Test;
-import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.factory.Nd4j;
 
 import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertTrue;
 
 /**
  *
@@ -32,7 +30,7 @@ import static org.junit.Assert.assertTrue;
 public class HistoryProcessorTest {
 
     @Test
-    public void testHistoryProcessor() throws Exception {
+    public void testHistoryProcessor() {
         HistoryProcessor.Configuration conf = HistoryProcessor.Configuration.builder()
                 .croppingHeight(2).croppingWidth(2).rescaledHeight(2).rescaledWidth(2).build();
         IHistoryProcessor hp = new HistoryProcessor(conf);
@@ -43,8 +41,6 @@ public class HistoryProcessorTest {
         hp.add(a);
         INDArray[] h = hp.getHistory();
         assertEquals(4, h.length);
-//        System.out.println(Arrays.toString(a.shape()));
-//        System.out.println(Arrays.toString(h[0].shape()));
         assertEquals(           1, h[0].shape()[0]);
         assertEquals(a.shape()[0], h[0].shape()[1]);
         assertEquals(a.shape()[1], h[0].shape()[2]);

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/learning/async/AsyncLearningTest.java

@@ -1,9 +1,32 @@
+/*******************************************************************************
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
 package org.deeplearning4j.rl4j.learning.async;
 
+import org.deeplearning4j.rl4j.learning.configuration.IAsyncLearningConfiguration;
 import org.deeplearning4j.rl4j.mdp.MDP;
 import org.deeplearning4j.rl4j.policy.IPolicy;
 import org.deeplearning4j.rl4j.space.DiscreteSpace;
-import org.deeplearning4j.rl4j.support.*;
+import org.deeplearning4j.rl4j.support.MockAsyncConfiguration;
+import org.deeplearning4j.rl4j.support.MockAsyncGlobal;
+import org.deeplearning4j.rl4j.support.MockEncodable;
+import org.deeplearning4j.rl4j.support.MockNeuralNet;
+import org.deeplearning4j.rl4j.support.MockPolicy;
+import org.deeplearning4j.rl4j.support.MockTrainingListener;
 import org.junit.Test;
 
 import static org.junit.Assert.assertEquals;
@@ -68,7 +91,7 @@ public class AsyncLearningTest {
 
 
     public static class TestContext {
-        MockAsyncConfiguration config = new MockAsyncConfiguration(1, 11, 0, 0, 0, 0,0, 0, 0, 0);
+        MockAsyncConfiguration config = new MockAsyncConfiguration(1L, 11, 0, 0, 0, 0,0, 0, 0, 0);
         public final MockAsyncGlobal asyncGlobal = new MockAsyncGlobal();
         public final MockPolicy policy = new MockPolicy();
         public final TestAsyncLearning sut = new TestAsyncLearning(config, asyncGlobal, policy);
@@ -82,11 +105,11 @@ public class AsyncLearningTest {
     }
 
     public static class TestAsyncLearning extends AsyncLearning<MockEncodable, Integer, DiscreteSpace, MockNeuralNet> {
-        private final AsyncConfiguration conf;
+        private final IAsyncLearningConfiguration conf;
         private final IAsyncGlobal asyncGlobal;
         private final IPolicy<MockEncodable, Integer> policy;
 
-        public TestAsyncLearning(AsyncConfiguration conf, IAsyncGlobal asyncGlobal, IPolicy<MockEncodable, Integer> policy) {
+        public TestAsyncLearning(IAsyncLearningConfiguration conf, IAsyncGlobal asyncGlobal, IPolicy<MockEncodable, Integer> policy) {
             this.conf = conf;
             this.asyncGlobal = asyncGlobal;
             this.policy = policy;
@@ -98,7 +121,7 @@ public class AsyncLearningTest {
         }
 
         @Override
-        public AsyncConfiguration getConfiguration() {
+        public IAsyncLearningConfiguration getConfiguration() {
             return conf;
         }
 

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/learning/async/AsyncThreadDiscreteTest.java

@@ -1,7 +1,25 @@
+/*******************************************************************************
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
 package org.deeplearning4j.rl4j.learning.async;
 
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
+import org.deeplearning4j.rl4j.learning.configuration.IAsyncLearningConfiguration;
 import org.deeplearning4j.rl4j.learning.listener.TrainingListenerList;
 import org.deeplearning4j.rl4j.mdp.MDP;
 import org.deeplearning4j.rl4j.observation.Observation;
@@ -32,7 +50,7 @@ public class AsyncThreadDiscreteTest {
         MockMDP mdpMock = new MockMDP(observationSpace);
         TrainingListenerList listeners = new TrainingListenerList();
         MockPolicy policyMock = new MockPolicy();
-        MockAsyncConfiguration config = new MockAsyncConfiguration(5, 100, 0, 0, 2, 5,0, 0, 0, 0);
+        MockAsyncConfiguration config = new MockAsyncConfiguration(5L, 100, 0,0, 0, 0, 0, 0, 2, 5);
         TestAsyncThreadDiscrete sut = new TestAsyncThreadDiscrete(asyncGlobalMock, mdpMock, listeners, 0, 0, policyMock, config, hpMock);
         sut.getLegacyMDPWrapper().setTransformProcess(MockMDP.buildTransformProcess(observationSpace.getShape(), hpConf.getSkipFrame(), hpConf.getHistoryLength()));
 
@@ -173,7 +191,7 @@ public class AsyncThreadDiscreteTest {
         }
 
         @Override
-        protected AsyncConfiguration getConf() {
+        protected IAsyncLearningConfiguration getConf() {
             return config;
         }
 

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/learning/async/AsyncThreadTest.java

@@ -3,12 +3,20 @@ package org.deeplearning4j.rl4j.learning.async;
 import lombok.AllArgsConstructor;
 import lombok.Getter;
 import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
+import org.deeplearning4j.rl4j.learning.configuration.IAsyncLearningConfiguration;
 import org.deeplearning4j.rl4j.learning.listener.TrainingListenerList;
 import org.deeplearning4j.rl4j.mdp.MDP;
 import org.deeplearning4j.rl4j.observation.Observation;
 import org.deeplearning4j.rl4j.policy.Policy;
 import org.deeplearning4j.rl4j.space.DiscreteSpace;
-import org.deeplearning4j.rl4j.support.*;
+import org.deeplearning4j.rl4j.support.MockAsyncConfiguration;
+import org.deeplearning4j.rl4j.support.MockAsyncGlobal;
+import org.deeplearning4j.rl4j.support.MockEncodable;
+import org.deeplearning4j.rl4j.support.MockHistoryProcessor;
+import org.deeplearning4j.rl4j.support.MockMDP;
+import org.deeplearning4j.rl4j.support.MockNeuralNet;
+import org.deeplearning4j.rl4j.support.MockObservationSpace;
+import org.deeplearning4j.rl4j.support.MockTrainingListener;
 import org.deeplearning4j.rl4j.util.IDataManager;
 import org.junit.Test;
 
@@ -16,7 +24,6 @@ import java.util.ArrayList;
 import java.util.List;
 
 import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNull;
 
 public class AsyncThreadTest {
 
@@ -126,7 +133,7 @@ public class AsyncThreadTest {
         public final MockNeuralNet neuralNet = new MockNeuralNet();
         public final MockObservationSpace observationSpace = new MockObservationSpace();
         public final MockMDP mdp = new MockMDP(observationSpace);
-        public final MockAsyncConfiguration config = new MockAsyncConfiguration(5, 10, 0, 0, 10, 0, 0, 0, 0, 0);
+        public final MockAsyncConfiguration config = new MockAsyncConfiguration(5L, 10, 0, 0, 0, 0, 0, 0, 10, 0);
         public final TrainingListenerList listeners = new TrainingListenerList();
         public final MockTrainingListener listener = new MockTrainingListener();
         public final IHistoryProcessor.Configuration hpConf = new IHistoryProcessor.Configuration(5, 4, 4, 4, 4, 0, 0, 2);
@@ -149,11 +156,11 @@ public class AsyncThreadTest {
 
         private final MockAsyncGlobal asyncGlobal;
         private final MockNeuralNet neuralNet;
-        private final AsyncConfiguration conf;
+        private final IAsyncLearningConfiguration conf;
 
         private final List<TrainSubEpochParams> trainSubEpochParams = new ArrayList<TrainSubEpochParams>();
 
-        public MockAsyncThread(MockAsyncGlobal asyncGlobal, int threadNumber, MockNeuralNet neuralNet, MDP mdp, AsyncConfiguration conf, TrainingListenerList listeners) {
+        public MockAsyncThread(MockAsyncGlobal asyncGlobal, int threadNumber, MockNeuralNet neuralNet, MDP mdp, IAsyncLearningConfiguration conf, TrainingListenerList listeners) {
             super(asyncGlobal, mdp, listeners, threadNumber, 0);
 
             this.asyncGlobal = asyncGlobal;
@@ -184,7 +191,7 @@ public class AsyncThreadTest {
         }
 
         @Override
-        protected AsyncConfiguration getConf() {
+        protected IAsyncLearningConfiguration getConf() {
             return conf;
         }
 

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/learning/async/a3c/discrete/A3CThreadDiscreteTest.java

@@ -1,11 +1,27 @@
+/*******************************************************************************
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
 package org.deeplearning4j.rl4j.learning.async.a3c.discrete;
 
 import org.deeplearning4j.nn.api.NeuralNetwork;
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
 import org.deeplearning4j.rl4j.learning.async.MiniTrans;
-import org.deeplearning4j.rl4j.learning.async.nstep.discrete.AsyncNStepQLearningDiscrete;
+import org.deeplearning4j.rl4j.learning.configuration.A3CLearningConfiguration;
-import org.deeplearning4j.rl4j.learning.async.nstep.discrete.AsyncNStepQLearningThreadDiscrete;
 import org.deeplearning4j.rl4j.network.NeuralNet;
 import org.deeplearning4j.rl4j.network.ac.IActorCritic;
 import org.deeplearning4j.rl4j.support.*;
@@ -31,7 +47,7 @@ public class A3CThreadDiscreteTest {
         double gamma = 0.9;
         MockObservationSpace observationSpace = new MockObservationSpace();
         MockMDP mdpMock = new MockMDP(observationSpace);
-        A3CDiscrete.A3CConfiguration config = new A3CDiscrete.A3CConfiguration(0, 0, 0, 0, 0, 0, 0, gamma, 0);
+        A3CLearningConfiguration config = A3CLearningConfiguration.builder().gamma(0.9).build();
         MockActorCritic actorCriticMock = new MockActorCritic();
         IHistoryProcessor.Configuration hpConf = new IHistoryProcessor.Configuration(5, 1, 1, 1, 1, 0, 0, 2);
         MockAsyncGlobal<IActorCritic> asyncGlobalMock = new MockAsyncGlobal<IActorCritic>(actorCriticMock);
@@ -54,9 +70,9 @@ public class A3CThreadDiscreteTest {
                     Nd4j.zeros(5)
             };
             output[0].putScalar(i, outputs[i]);
-            minitransList.push(new MiniTrans<Integer>(obs, i, output, rewards[i]));
+            minitransList.push(new MiniTrans<>(obs, i, output, rewards[i]));
         }
-        minitransList.push(new MiniTrans<Integer>(null, 0, null, 4.0)); // The special batch-ending MiniTrans
+        minitransList.push(new MiniTrans<>(null, 0, null, 4.0)); // The special batch-ending MiniTrans
 
         // Act
         sut.calcGradient(actorCriticMock, minitransList);

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/learning/async/nstep/discrete/AsyncNStepQLearningThreadDiscreteTest.java

@@ -1,7 +1,24 @@
+/*******************************************************************************
+ * Copyright (c) 2015-2020 Skymind, Inc.
+ *
+ *  This program and the accompanying materials are made available under the
+ *  terms of the Apache License, Version 2.0 which is available at
+ *  https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *  WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *  License for the specific language governing permissions and limitations
+ *  under the License.
+ *
+ *  SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
 package org.deeplearning4j.rl4j.learning.async.nstep.discrete;
 
 import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
 import org.deeplearning4j.rl4j.learning.async.MiniTrans;
+import org.deeplearning4j.rl4j.learning.configuration.AsyncQLearningConfiguration;
 import org.deeplearning4j.rl4j.support.*;
 import org.junit.Test;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -19,7 +36,7 @@ public class AsyncNStepQLearningThreadDiscreteTest {
         double gamma = 0.9;
         MockObservationSpace observationSpace = new MockObservationSpace();
         MockMDP mdpMock = new MockMDP(observationSpace);
-        AsyncNStepQLearningDiscrete.AsyncNStepQLConfiguration config = new AsyncNStepQLearningDiscrete.AsyncNStepQLConfiguration(0, 0, 0, 0, 0, 0, 0, 0, gamma, 0, 0, 0);
+        AsyncQLearningConfiguration config = AsyncQLearningConfiguration.builder().gamma(gamma).build();
         MockDQN dqnMock = new MockDQN();
         IHistoryProcessor.Configuration hpConf = new IHistoryProcessor.Configuration(5, 1, 1, 1, 1, 0, 0, 2);
         MockAsyncGlobal asyncGlobalMock = new MockAsyncGlobal(dqnMock);
@@ -42,9 +59,9 @@ public class AsyncNStepQLearningThreadDiscreteTest {
                     Nd4j.zeros(5)
             };
             output[0].putScalar(i, outputs[i]);
-            minitransList.push(new MiniTrans<Integer>(obs, i, output, rewards[i]));
+            minitransList.push(new MiniTrans<>(obs, i, output, rewards[i]));
         }
-        minitransList.push(new MiniTrans<Integer>(null, 0, null, 4.0)); // The special batch-ending MiniTrans
+        minitransList.push(new MiniTrans<>(null, 0, null, 4.0)); // The special batch-ending MiniTrans
 
         // Act
         sut.calcGradient(dqnMock, minitransList);

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/learning/sync/SyncLearningTest.java

@@ -1,6 +1,26 @@
+/*******************************************************************************
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
 package org.deeplearning4j.rl4j.learning.sync;
 
 import lombok.Getter;
+import org.deeplearning4j.rl4j.learning.configuration.ILearningConfiguration;
+import org.deeplearning4j.rl4j.learning.configuration.LearningConfiguration;
+import org.deeplearning4j.rl4j.learning.configuration.QLearningConfiguration;
 import org.deeplearning4j.rl4j.learning.sync.qlearning.QLearning;
 import org.deeplearning4j.rl4j.learning.sync.support.MockStatEntry;
 import org.deeplearning4j.rl4j.mdp.MDP;
@@ -17,7 +37,7 @@ public class SyncLearningTest {
     @Test
     public void when_training_expect_listenersToBeCalled() {
         // Arrange
-        QLearning.QLConfiguration lconfig = QLearning.QLConfiguration.builder().maxStep(10).build();
+        QLearningConfiguration lconfig = QLearningConfiguration.builder().maxStep(10).build();
         MockTrainingListener listener = new MockTrainingListener();
         MockSyncLearning sut = new MockSyncLearning(lconfig);
         sut.addListener(listener);
@@ -34,7 +54,7 @@ public class SyncLearningTest {
     @Test
     public void when_trainingStartCanContinueFalse_expect_trainingStopped() {
         // Arrange
-        QLearning.QLConfiguration lconfig = QLearning.QLConfiguration.builder().maxStep(10).build();
+        QLearningConfiguration lconfig = QLearningConfiguration.builder().maxStep(10).build();
         MockTrainingListener listener = new MockTrainingListener();
         MockSyncLearning sut = new MockSyncLearning(lconfig);
         sut.addListener(listener);
@@ -52,7 +72,7 @@ public class SyncLearningTest {
     @Test
     public void when_newEpochCanContinueFalse_expect_trainingStopped() {
         // Arrange
-        QLearning.QLConfiguration lconfig = QLearning.QLConfiguration.builder().maxStep(10).build();
+        QLearningConfiguration lconfig = QLearningConfiguration.builder().maxStep(10).build();
         MockTrainingListener listener = new MockTrainingListener();
         MockSyncLearning sut = new MockSyncLearning(lconfig);
         sut.addListener(listener);
@@ -70,7 +90,7 @@ public class SyncLearningTest {
     @Test
     public void when_epochTrainingResultCanContinueFalse_expect_trainingStopped() {
         // Arrange
-        QLearning.QLConfiguration lconfig = QLearning.QLConfiguration.builder().maxStep(10).build();
+        LearningConfiguration lconfig = QLearningConfiguration.builder().maxStep(10).build();
         MockTrainingListener listener = new MockTrainingListener();
         MockSyncLearning sut = new MockSyncLearning(lconfig);
         sut.addListener(listener);
@@ -87,12 +107,12 @@ public class SyncLearningTest {
 
     public static class MockSyncLearning extends SyncLearning {
 
-        private final LConfiguration conf;
+        private final ILearningConfiguration conf;
 
         @Getter
         private int currentEpochStep = 0;
 
-        public MockSyncLearning(LConfiguration conf) {
+        public MockSyncLearning(ILearningConfiguration conf) {
             this.conf = conf;
         }
 
@@ -119,7 +139,7 @@ public class SyncLearningTest {
         }
 
         @Override
-        public LConfiguration getConfiguration() {
+        public ILearningConfiguration getConfiguration() {
             return conf;
         }
 

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/learning/sync/qlearning/QLearningConfigurationTest.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -17,36 +18,24 @@
 package org.deeplearning4j.rl4j.learning.sync.qlearning;
 
 import com.fasterxml.jackson.databind.ObjectMapper;
+import org.deeplearning4j.rl4j.learning.configuration.QLearningConfiguration;
 import org.junit.Rule;
 import org.junit.Test;
 import org.junit.rules.ExpectedException;
 
-public class QLConfigurationTest {
+public class QLearningConfigurationTest {
     @Rule
     public ExpectedException thrown = ExpectedException.none();
 
     @Test
     public void serialize() throws Exception {
         ObjectMapper mapper = new ObjectMapper();
-        QLearning.QLConfiguration qlConfiguration =
+
-                new QLearning.QLConfiguration(
+        QLearningConfiguration qLearningConfiguration = QLearningConfiguration.builder()
-                        123,    //Random seed
+                .build();
-                        200,    //Max step By epoch
-                        8000, //Max step
-                        150000, //Max size of experience replay
-                        32,     //size of batches
-                        500,    //target update (hard)
-                        10,     //num step noop warmup
-                        0.01,   //reward scaling
-                        0.99,   //gamma
-                        1.0,    //td error clipping
-                        0.1f,   //min epsilon
-                        10000,   //num step for eps greedy anneal
-                        true    //double DQN
-                );
 
         // Should not throw..
-        String json = mapper.writeValueAsString(qlConfiguration);
+        String json = mapper.writeValueAsString(qLearningConfiguration);
-        QLearning.QLConfiguration cnf = mapper.readValue(json, QLearning.QLConfiguration.class);
+        QLearningConfiguration cnf = mapper.readValue(json, QLearningConfiguration.class);
     }
 }

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/learning/sync/qlearning/discrete/QLearningDiscreteTest.java

@@ -1,6 +1,24 @@
+/*******************************************************************************
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
 package org.deeplearning4j.rl4j.learning.sync.qlearning.discrete;
 
 import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
+import org.deeplearning4j.rl4j.learning.configuration.QLearningConfiguration;
 import org.deeplearning4j.rl4j.learning.sync.IExpReplay;
 import org.deeplearning4j.rl4j.learning.sync.Transition;
 import org.deeplearning4j.rl4j.learning.sync.qlearning.QLearning;
@@ -27,7 +45,7 @@ public class QLearningDiscreteTest {
         // Arrange
         MockObservationSpace observationSpace = new MockObservationSpace();
         MockDQN dqn = new MockDQN();
-        MockRandom random = new MockRandom(new double[] {
+        MockRandom random = new MockRandom(new double[]{
                 0.7309677600860596,
                 0.8314409852027893,
                 0.2405363917350769,
@@ -36,14 +54,26 @@ public class QLearningDiscreteTest {
                 0.3090505599975586,
                 0.5504369735717773,
                 0.11700659990310669
-            },
+        },
-            new int[] { 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4 });
+                new int[]{1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4});
         MockMDP mdp = new MockMDP(observationSpace, random);
 
         int initStepCount = 8;
 
-        QLearning.QLConfiguration conf = new QLearning.QLConfiguration(0, 24, 0, 5, 1, 1000,
+        QLearningConfiguration conf = QLearningConfiguration.builder()
-                initStepCount, 1.0, 0, 0, 0, 0, true);
+                .seed(0L)
+                .maxEpochStep(24)
+                .maxStep(0)
+                .expRepMaxSize(5).batchSize(1).targetDqnUpdateFreq(1000)
+                .updateStart(initStepCount)
+                .rewardFactor(1.0)
+                .gamma(0)
+                .errorClamp(0)
+                .minEpsilon(0)
+                .epsilonNbStep(0)
+                .doubleDQN(true)
+                .build();
+
         MockDataManager dataManager = new MockDataManager(false);
         MockExpReplay expReplay = new MockExpReplay();
         TestQLearningDiscrete sut = new TestQLearningDiscrete(mdp, dqn, conf, dataManager, expReplay, 10, random);
@@ -58,9 +88,9 @@ public class QLearningDiscreteTest {
 
         // Assert
         // HistoryProcessor calls
-        double[] expectedRecords = new double[] { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0 };
+        double[] expectedRecords = new double[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0};
         assertEquals(expectedRecords.length, hp.recordCalls.size());
-        for(int i = 0; i < expectedRecords.length; ++i) {
+        for (int i = 0; i < expectedRecords.length; ++i) {
             assertEquals(expectedRecords[i], hp.recordCalls.get(i).getDouble(0), 0.0001);
         }
 
@@ -72,59 +102,59 @@ public class QLearningDiscreteTest {
         assertEquals(123.0, dqn.fitParams.get(0).getFirst().getDouble(0), 0.001);
         assertEquals(234.0, dqn.fitParams.get(0).getSecond().getDouble(0), 0.001);
         assertEquals(14, dqn.outputParams.size());
-        double[][] expectedDQNOutput = new double[][] {
+        double[][] expectedDQNOutput = new double[][]{
-                new double[] { 0.0, 2.0, 4.0, 6.0, 8.0 },
+                new double[]{0.0, 2.0, 4.0, 6.0, 8.0},
-                new double[] { 2.0, 4.0, 6.0, 8.0, 10.0 },
+                new double[]{2.0, 4.0, 6.0, 8.0, 10.0},
-                new double[] { 2.0, 4.0, 6.0, 8.0, 10.0 },
+                new double[]{2.0, 4.0, 6.0, 8.0, 10.0},
-                new double[] { 4.0, 6.0, 8.0, 10.0, 12.0 },
+                new double[]{4.0, 6.0, 8.0, 10.0, 12.0},
-                new double[] { 6.0, 8.0, 10.0, 12.0, 14.0 },
+                new double[]{6.0, 8.0, 10.0, 12.0, 14.0},
-                new double[] { 6.0, 8.0, 10.0, 12.0, 14.0 },
+                new double[]{6.0, 8.0, 10.0, 12.0, 14.0},
-                new double[] { 8.0, 10.0, 12.0, 14.0, 16.0 },
+                new double[]{8.0, 10.0, 12.0, 14.0, 16.0},
-                new double[] { 8.0, 10.0, 12.0, 14.0, 16.0 },
+                new double[]{8.0, 10.0, 12.0, 14.0, 16.0},
-                new double[] { 10.0, 12.0, 14.0, 16.0, 18.0 },
+                new double[]{10.0, 12.0, 14.0, 16.0, 18.0},
-                new double[] { 10.0, 12.0, 14.0, 16.0, 18.0 },
+                new double[]{10.0, 12.0, 14.0, 16.0, 18.0},
-                new double[] { 12.0, 14.0, 16.0, 18.0, 20.0 },
+                new double[]{12.0, 14.0, 16.0, 18.0, 20.0},
-                new double[] { 12.0, 14.0, 16.0, 18.0, 20.0 },
+                new double[]{12.0, 14.0, 16.0, 18.0, 20.0},
-                new double[] { 14.0, 16.0, 18.0, 20.0, 22.0 },
+                new double[]{14.0, 16.0, 18.0, 20.0, 22.0},
-                new double[] { 14.0, 16.0, 18.0, 20.0, 22.0 },
+                new double[]{14.0, 16.0, 18.0, 20.0, 22.0},
         };
-        for(int i = 0; i < expectedDQNOutput.length; ++i) {
+        for (int i = 0; i < expectedDQNOutput.length; ++i) {
             INDArray outputParam = dqn.outputParams.get(i);
 
             assertEquals(5, outputParam.shape()[1]);
             assertEquals(1, outputParam.shape()[2]);
 
             double[] expectedRow = expectedDQNOutput[i];
-            for(int j = 0; j < expectedRow.length; ++j) {
+            for (int j = 0; j < expectedRow.length; ++j) {
-                assertEquals("row: "+ i + " col: " + j, expectedRow[j], 255.0 * outputParam.getDouble(j), 0.00001);
+                assertEquals("row: " + i + " col: " + j, expectedRow[j], 255.0 * outputParam.getDouble(j), 0.00001);
             }
         }
 
         // MDP calls
-        assertArrayEquals(new Integer[] {0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 4, 4, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4 }, mdp.actions.toArray());
+        assertArrayEquals(new Integer[]{0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 4, 4, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4}, mdp.actions.toArray());
 
         // ExpReplay calls
-        double[] expectedTrRewards = new double[] { 9.0, 21.0, 25.0, 29.0, 33.0, 37.0, 41.0 };
+        double[] expectedTrRewards = new double[]{9.0, 21.0, 25.0, 29.0, 33.0, 37.0, 41.0};
-        int[] expectedTrActions = new int[] { 1, 4, 2, 4, 4, 4, 4, 4 };
+        int[] expectedTrActions = new int[]{1, 4, 2, 4, 4, 4, 4, 4};
-        double[] expectedTrNextObservation = new double[] { 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0 };
+        double[] expectedTrNextObservation = new double[]{2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0, 16.0};
-        double[][] expectedTrObservations = new double[][] {
+        double[][] expectedTrObservations = new double[][]{
-                new double[] { 0.0, 2.0, 4.0, 6.0, 8.0 },
+                new double[]{0.0, 2.0, 4.0, 6.0, 8.0},
-                new double[] { 2.0, 4.0, 6.0, 8.0, 10.0 },
+                new double[]{2.0, 4.0, 6.0, 8.0, 10.0},
-                new double[] { 4.0, 6.0, 8.0, 10.0, 12.0 },
+                new double[]{4.0, 6.0, 8.0, 10.0, 12.0},
-                new double[] { 6.0, 8.0, 10.0, 12.0, 14.0 },
+                new double[]{6.0, 8.0, 10.0, 12.0, 14.0},
-                new double[] { 8.0, 10.0, 12.0, 14.0, 16.0 },
+                new double[]{8.0, 10.0, 12.0, 14.0, 16.0},
-                new double[] { 10.0, 12.0, 14.0, 16.0, 18.0 },
+                new double[]{10.0, 12.0, 14.0, 16.0, 18.0},
-                new double[] { 12.0, 14.0, 16.0, 18.0, 20.0 },
+                new double[]{12.0, 14.0, 16.0, 18.0, 20.0},
-                new double[] { 14.0, 16.0, 18.0, 20.0, 22.0 },
+                new double[]{14.0, 16.0, 18.0, 20.0, 22.0},
         };
         assertEquals(expectedTrObservations.length, expReplay.transitions.size());
-        for(int i = 0; i < expectedTrRewards.length; ++i) {
+        for (int i = 0; i < expectedTrRewards.length; ++i) {
             Transition tr = expReplay.transitions.get(i);
             assertEquals(expectedTrRewards[i], tr.getReward(), 0.0001);
             assertEquals(expectedTrActions[i], tr.getAction());
             assertEquals(expectedTrNextObservation[i], 255.0 * tr.getNextObservation().getDouble(0), 0.0001);
-            for(int j = 0; j < expectedTrObservations[i].length; ++j) {
+            for (int j = 0; j < expectedTrObservations[i].length; ++j) {
-                assertEquals("row: "+ i + " col: " + j, expectedTrObservations[i][j], 255.0 * tr.getObservation().getData().getDouble(0, j, 0), 0.0001);
+                assertEquals("row: " + i + " col: " + j, expectedTrObservations[i][j], 255.0 * tr.getObservation().getData().getDouble(0, j, 0), 0.0001);
             }
         }
 
@@ -132,12 +162,12 @@ public class QLearningDiscreteTest {
         assertEquals(initStepCount + 16, result.getStepCounter());
         assertEquals(300.0, result.getReward(), 0.00001);
         assertTrue(dqn.hasBeenReset);
-        assertTrue(((MockDQN)sut.getTargetQNetwork()).hasBeenReset);
+        assertTrue(((MockDQN) sut.getTargetQNetwork()).hasBeenReset);
     }
 
     public static class TestQLearningDiscrete extends QLearningDiscrete<MockEncodable> {
         public TestQLearningDiscrete(MDP<MockEncodable, Integer, DiscreteSpace> mdp, IDQN dqn,
-                                     QLConfiguration conf, IDataManager dataManager, MockExpReplay expReplay,
+                                     QLearningConfiguration conf, IDataManager dataManager, MockExpReplay expReplay,
                                      int epsilonNbStep, Random rnd) {
             super(mdp, dqn, conf, epsilonNbStep, rnd);
             addListener(new DataManagerTrainingListener(dataManager));
@@ -146,10 +176,10 @@ public class QLearningDiscreteTest {
 
         @Override
         protected DataSet setTarget(ArrayList<Transition<Integer>> transitions) {
-            return new org.nd4j.linalg.dataset.DataSet(Nd4j.create(new double[] { 123.0 }), Nd4j.create(new double[] { 234.0 }));
+            return new org.nd4j.linalg.dataset.DataSet(Nd4j.create(new double[]{123.0}), Nd4j.create(new double[]{234.0}));
         }
 
-        public void setExpReplay(IExpReplay<Integer> exp){
+        public void setExpReplay(IExpReplay<Integer> exp) {
             this.expReplay = exp;
         }
 

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/network/ac/ActorCriticTest.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -16,6 +17,7 @@
 
 package org.deeplearning4j.rl4j.network.ac;
 
+import org.deeplearning4j.rl4j.network.configuration.ActorCriticDenseNetworkConfiguration;
 import org.junit.Test;
 import org.nd4j.linalg.activations.impl.ActivationSoftmax;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -29,30 +31,31 @@ import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertTrue;
 
 /**
- *
  * @author saudet
  */
 public class ActorCriticTest {
 
-    public static ActorCriticFactorySeparateStdDense.Configuration NET_CONF =
+    public static ActorCriticDenseNetworkConfiguration NET_CONF =
-            new ActorCriticFactorySeparateStdDense.Configuration(
+            ActorCriticDenseNetworkConfiguration.builder()
-                    4,         //number of layers
+                    .numLayers(4)
-                    32,        //number of hidden nodes
+                    .numHiddenNodes(32)
-                    0.001,     //l2 regularization
+                    .l2(0.001)
-                    new RmsProp(0.0005), null, false
+                    .updater(new RmsProp(0.0005))
-            );
+                    .useLSTM(false)
+                    .build();
 
-    public static ActorCriticFactoryCompGraphStdDense.Configuration NET_CONF_CG =
+    public static ActorCriticDenseNetworkConfiguration NET_CONF_CG =
-            new ActorCriticFactoryCompGraphStdDense.Configuration(
+            ActorCriticDenseNetworkConfiguration.builder()
-                    2,         //number of layers
+                    .numLayers(2)
-                    128,       //number of hidden nodes
+                    .numHiddenNodes(128)
-                    0.00001,   //l2 regularization
+                    .l2(0.00001)
-                    new RmsProp(0.005), null, true
+                    .updater(new RmsProp(0.005))
-            );
+                    .useLSTM(true)
+                    .build();
 
     @Test
     public void testModelLoadSave() throws IOException {
-        ActorCriticSeparate acs = new ActorCriticFactorySeparateStdDense(NET_CONF).buildActorCritic(new int[] {7}, 5);
+        ActorCriticSeparate acs = new ActorCriticFactorySeparateStdDense(NET_CONF).buildActorCritic(new int[]{7}, 5);
 
         File fileValue = File.createTempFile("rl4j-value-", ".model");
         File filePolicy = File.createTempFile("rl4j-policy-", ".model");
@@ -63,7 +66,7 @@ public class ActorCriticTest {
         assertEquals(acs.valueNet, acs2.valueNet);
         assertEquals(acs.policyNet, acs2.policyNet);
 
-        ActorCriticCompGraph accg = new ActorCriticFactoryCompGraphStdDense(NET_CONF_CG).buildActorCritic(new int[] {37}, 43);
+        ActorCriticCompGraph accg = new ActorCriticFactoryCompGraphStdDense(NET_CONF_CG).buildActorCritic(new int[]{37}, 43);
 
         File file = File.createTempFile("rl4j-cg-", ".model");
         accg.save(file.getAbsolutePath());
@@ -83,15 +86,15 @@ public class ActorCriticTest {
 
         for (double i = eps; i < n; i++) {
             for (double j = eps; j < n; j++) {
-                INDArray labels = Nd4j.create(new double[] {i / n, 1 - i / n}, new long[]{1,2});
+                INDArray labels = Nd4j.create(new double[]{i / n, 1 - i / n}, new long[]{1, 2});
-                INDArray output = Nd4j.create(new double[] {j / n, 1 - j / n}, new long[]{1,2});
+                INDArray output = Nd4j.create(new double[]{j / n, 1 - j / n}, new long[]{1, 2});
                 INDArray gradient = loss.computeGradient(labels, output, activation, null);
 
-                output = Nd4j.create(new double[] {j / n, 1 - j / n}, new long[]{1,2});
+                output = Nd4j.create(new double[]{j / n, 1 - j / n}, new long[]{1, 2});
                 double score = loss.computeScore(labels, output, activation, null, false);
-                INDArray output1 = Nd4j.create(new double[] {j / n + eps, 1 - j / n}, new long[]{1,2});
+                INDArray output1 = Nd4j.create(new double[]{j / n + eps, 1 - j / n}, new long[]{1, 2});
                 double score1 = loss.computeScore(labels, output1, activation, null, false);
-                INDArray output2 = Nd4j.create(new double[] {j / n, 1 - j / n + eps}, new long[]{1,2});
+                INDArray output2 = Nd4j.create(new double[]{j / n, 1 - j / n + eps}, new long[]{1, 2});
                 double score2 = loss.computeScore(labels, output2, activation, null, false);
 
                 double gradient1 = (score1 - score) / eps;

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/network/dqn/DQNTest.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -16,6 +17,7 @@
 
 package org.deeplearning4j.rl4j.network.dqn;
 
+import org.deeplearning4j.rl4j.network.configuration.DQNDenseNetworkConfiguration;
 import org.junit.Test;
 import org.nd4j.linalg.learning.config.RmsProp;
 
@@ -25,22 +27,20 @@ import java.io.IOException;
 import static org.junit.Assert.assertEquals;
 
 /**
- *
  * @author saudet
  */
 public class DQNTest {
 
-    public static DQNFactoryStdDense.Configuration NET_CONF =
+    private static DQNDenseNetworkConfiguration NET_CONF =
-            new DQNFactoryStdDense.Configuration(
+            DQNDenseNetworkConfiguration.builder().numLayers(3)
-                    3,         //number of layers
+                    .numHiddenNodes(16)
-                    16,        //number of hidden nodes
+                    .l2(0.001)
-                    0.001,     //l2 regularization
+                    .updater(new RmsProp(0.0005))
-                    new RmsProp(0.0005), null
+                    .build();
-            );
 
     @Test
     public void testModelLoadSave() throws IOException {
-        DQN dqn = new DQNFactoryStdDense(NET_CONF).buildDQN(new int[] {42}, 13);
+        DQN dqn = new DQNFactoryStdDense(NET_CONF).buildDQN(new int[]{42}, 13);
 
         File file = File.createTempFile("rl4j-dqn-", ".model");
         dqn.save(file.getAbsolutePath());

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/observation/transform/TransformProcessTest.java

@@ -128,7 +128,7 @@ public class TransformProcessTest {
 
         // Assert
         assertFalse(result.isSkipped());
-        assertEquals(1, result.getData().shape().length);
+        assertEquals(2, result.getData().shape().length);
         assertEquals(1, result.getData().shape()[0]);
         assertEquals(-10.0, result.getData().getDouble(0), 0.00001);
     }

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/policy/PolicyTest.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -24,16 +25,18 @@ import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
 import org.deeplearning4j.rl4j.learning.Learning;
-import org.deeplearning4j.rl4j.learning.sync.qlearning.QLearning;
+import org.deeplearning4j.rl4j.learning.configuration.QLearningConfiguration;
-import org.deeplearning4j.rl4j.learning.sync.qlearning.discrete.QLearningDiscreteTest;
-import org.deeplearning4j.rl4j.mdp.MDP;
 import org.deeplearning4j.rl4j.network.NeuralNet;
 import org.deeplearning4j.rl4j.network.ac.IActorCritic;
 import org.deeplearning4j.rl4j.observation.Observation;
 import org.deeplearning4j.rl4j.space.ActionSpace;
-import org.deeplearning4j.rl4j.space.DiscreteSpace;
+import org.deeplearning4j.rl4j.support.MockDQN;
-import org.deeplearning4j.rl4j.space.Encodable;
+import org.deeplearning4j.rl4j.support.MockEncodable;
-import org.deeplearning4j.rl4j.support.*;
+import org.deeplearning4j.rl4j.support.MockHistoryProcessor;
+import org.deeplearning4j.rl4j.support.MockMDP;
+import org.deeplearning4j.rl4j.support.MockNeuralNet;
+import org.deeplearning4j.rl4j.support.MockObservationSpace;
+import org.deeplearning4j.rl4j.support.MockRandom;
 import org.deeplearning4j.rl4j.util.LegacyMDPWrapper;
 import org.junit.Test;
 import org.nd4j.linalg.activations.Activation;
@@ -43,8 +46,6 @@ import org.nd4j.linalg.lossfunctions.LossFunctions;
 
 import java.io.IOException;
 import java.io.OutputStream;
-import java.util.ArrayList;
-import java.util.List;
 
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertNotNull;
@@ -186,8 +187,22 @@ public class PolicyTest {
             new int[] { 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4 });
         MockMDP mdp = new MockMDP(observationSpace, 30, random);
 
-        QLearning.QLConfiguration conf = new QLearning.QLConfiguration(0, 0, 0, 5, 1, 0,
+        QLearningConfiguration conf = QLearningConfiguration.builder()
-                0, 1.0, 0, 0, 0, 0, true);
+                .seed(0L)
+                .maxEpochStep(0)
+                .maxStep(0)
+                .expRepMaxSize(5)
+                .batchSize(1)
+                .targetDqnUpdateFreq(0)
+                .updateStart(0)
+                .rewardFactor(1.0)
+                .gamma(0)
+                .errorClamp(0)
+                .minEpsilon(0)
+                .epsilonNbStep(0)
+                .doubleDQN(true)
+                .build();
+
         MockNeuralNet nnMock = new MockNeuralNet();
         IHistoryProcessor.Configuration hpConf = new IHistoryProcessor.Configuration(5, 4, 4, 4, 4, 0, 0, 2);
         MockRefacPolicy sut = new MockRefacPolicy(nnMock, observationSpace.getShape(), hpConf.getSkipFrame(), hpConf.getHistoryLength());

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/support/MockAsyncConfiguration.java

@@ -1,22 +1,37 @@
+/*******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
 package org.deeplearning4j.rl4j.support;
 
 import lombok.AllArgsConstructor;
-import lombok.Getter;
 import lombok.Value;
-import org.deeplearning4j.rl4j.learning.async.AsyncConfiguration;
+import org.deeplearning4j.rl4j.learning.configuration.IAsyncLearningConfiguration;
 
-@AllArgsConstructor
 @Value
-public class MockAsyncConfiguration implements AsyncConfiguration {
+@AllArgsConstructor
+public class MockAsyncConfiguration implements IAsyncLearningConfiguration {
 
-    private Integer seed;
+    private Long seed;
     private int maxEpochStep;
     private int maxStep;
-    private int numThread;
-    private int nstep;
-    private int targetDqnUpdateFreq;
     private int updateStart;
     private double rewardFactor;
     private double gamma;
     private double errorClamp;
+    private int numThreads;
+    private int nStep;
+    private int learnerUpdateFrequency;
 }

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/util/DataManagerTrainingListenerTest.java

@@ -1,3 +1,20 @@
+/*******************************************************************************
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://www.apache.org/licenses/LICENSE-2.0.
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ * License for the specific language governing permissions and limitations
+ * under the License.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+
 package org.deeplearning4j.rl4j.util;
 
 import lombok.Getter;
@@ -5,6 +22,7 @@ import lombok.Setter;
 import org.deeplearning4j.rl4j.learning.IEpochTrainer;
 import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
 import org.deeplearning4j.rl4j.learning.ILearning;
+import org.deeplearning4j.rl4j.learning.configuration.ILearningConfiguration;
 import org.deeplearning4j.rl4j.learning.listener.TrainingListener;
 import org.deeplearning4j.rl4j.learning.sync.support.MockStatEntry;
 import org.deeplearning4j.rl4j.mdp.MDP;
@@ -162,7 +180,7 @@ public class DataManagerTrainingListenerTest {
         }
 
         @Override
-        public LConfiguration getConfiguration() {
+        public ILearningConfiguration getConfiguration() {
             return null;
         }
 

rl4j/rl4j-malmo/src/main/java/org/deeplearning4j/malmo/MalmoObservationSpaceGrid.java

@@ -1,5 +1,6 @@
 /*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
+ * Copyright (c) 2015-2019 Skymind, Inc.
+ * Copyright (c) 2020 Konduit K.K.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
@@ -16,19 +17,18 @@
 
 package org.deeplearning4j.malmo;
 
-import java.util.HashMap;
+import com.microsoft.msr.malmo.TimestampedStringVector;
-
+import com.microsoft.msr.malmo.WorldState;
 import org.json.JSONArray;
 import org.json.JSONObject;
-
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.factory.Nd4j;
 
-import com.microsoft.msr.malmo.TimestampedStringVector;
+import java.util.HashMap;
-import com.microsoft.msr.malmo.WorldState;
 
 /**
  * Observation space that contains a grid of Minecraft blocks
+ *
  * @author howard-abrams (howard.abrams@ca.com) on 1/12/17.
  */
 public class MalmoObservationSpaceGrid extends MalmoObservationSpace {
@@ -44,10 +44,10 @@ public class MalmoObservationSpaceGrid extends MalmoObservationSpace {
     /**
      * Construct observation space from a array of blocks policy should distinguish between.
      *
-     * @param name Name given to Grid element in mission specification
+     * @param name   Name given to Grid element in mission specification
-     * @param xSize total x size of grid
+     * @param xSize  total x size of grid
-     * @param ySize total y size of grid
+     * @param ySize  total y size of grid
-     * @param zSize total z size of grid
+     * @param zSize  total z size of grid
      * @param blocks Array of block names to distinguish between. Supports combination of individual strings and/or arrays of strings to map multiple block types to a single observation value. If not specified, it will dynamically map block names to integers - however, because these will be mapped as they are seen, different missions may have different mappings!
      */
     public MalmoObservationSpaceGrid(String name, int xSize, int ySize, int zSize, Object... blocks) {
@@ -78,7 +78,7 @@ public class MalmoObservationSpaceGrid extends MalmoObservationSpace {
 
     @Override
     public int[] getShape() {
-        return new int[] {totalSize};
+        return new int[]{totalSize};
     }
 
     @Override