RL4J Added listener pattern to SyncLearning (#8050)
* Added listener pattern to SyncLearning Signed-off-by: Alexandre Boulanger <aboulang2002@yahoo.com> * Did requested changes Signed-off-by: Alexandre Boulanger <aboulang2002@yahoo.com>master
Alexandre Boulanger
Alex Black
parent
0527ab8d98
commit
b2145ca780
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@ -125,8 +125,6 @@ public abstract class Learning<O extends Encodable, A, AS extends ActionSpace<A>
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return nshape;
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}
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protected abstract IDataManager getDataManager();
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public abstract NN getNeuralNet();
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public int incrementStep() {
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@ -21,6 +21,7 @@ import org.deeplearning4j.rl4j.learning.Learning;
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import org.deeplearning4j.rl4j.network.NeuralNet;
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import org.deeplearning4j.rl4j.space.ActionSpace;
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import org.deeplearning4j.rl4j.space.Encodable;
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import org.deeplearning4j.rl4j.util.IDataManager;
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import org.nd4j.linalg.factory.Nd4j;
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/**
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@ -36,6 +37,7 @@ import org.nd4j.linalg.factory.Nd4j;
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public abstract class AsyncLearning<O extends Encodable, A, AS extends ActionSpace<A>, NN extends NeuralNet>
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extends Learning<O, A, AS, NN> {
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protected abstract IDataManager getDataManager();
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public AsyncLearning(AsyncConfiguration conf) {
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super(conf);
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@ -18,67 +18,132 @@ package org.deeplearning4j.rl4j.learning.sync;
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import lombok.extern.slf4j.Slf4j;
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import org.deeplearning4j.rl4j.learning.Learning;
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import org.deeplearning4j.rl4j.learning.sync.listener.SyncTrainingEpochEndEvent;
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import org.deeplearning4j.rl4j.learning.sync.listener.SyncTrainingEvent;
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import org.deeplearning4j.rl4j.learning.sync.listener.SyncTrainingListener;
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import org.deeplearning4j.rl4j.network.NeuralNet;
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import org.deeplearning4j.rl4j.space.ActionSpace;
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import org.deeplearning4j.rl4j.space.Encodable;
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import org.deeplearning4j.rl4j.util.Constants;
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import org.deeplearning4j.rl4j.util.IDataManager;
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import java.util.ArrayList;
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import java.util.List;
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/**
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* @author rubenfiszel (ruben.fiszel@epfl.ch) on 8/3/16.
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*
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* Mother class and useful factorisations for all training methods that
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* are not asynchronous.
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*
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* @author rubenfiszel (ruben.fiszel@epfl.ch) on 8/3/16.
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* @author Alexandre Boulanger
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*/
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@Slf4j
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public abstract class SyncLearning<O extends Encodable, A, AS extends ActionSpace<A>, NN extends NeuralNet>
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extends Learning<O, A, AS, NN> {
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private int lastSave = -Constants.MODEL_SAVE_FREQ;
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private List<SyncTrainingListener> listeners = new ArrayList<>();
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public SyncLearning(LConfiguration conf) {
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super(conf);
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}
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/**
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* Add a listener at the end of the listener list.
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*
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* @param listener
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*/
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public void addListener(SyncTrainingListener listener) {
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listeners.add(listener);
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}
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/**
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* This method will train the model<p>
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* The training stop when:<br>
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* - the number of steps reaches the maximum defined in the configuration (see {@link LConfiguration#getMaxStep() LConfiguration.getMaxStep()})<br>
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* OR<br>
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* - a listener explicitly stops it<br>
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* <p>
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* Listeners<br>
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* For a given event, the listeners are called sequentially in same the order as they were added. If one listener
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* returns {@link SyncTrainingListener.ListenerResponse SyncTrainingListener.ListenerResponse.STOP}, the remaining listeners in the list won't be called.<br>
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* Events:
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* <ul>
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* <li>{@link SyncTrainingListener#onTrainingStart(SyncTrainingEvent) onTrainingStart()} is called once when the training starts.</li>
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* <li>{@link SyncTrainingListener#onEpochStart(SyncTrainingEvent) onEpochStart()} and {@link SyncTrainingListener#onEpochEnd(SyncTrainingEpochEndEvent) onEpochEnd()} are called for every epoch. onEpochEnd will not be called if onEpochStart stops the training</li>
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* <li>{@link SyncTrainingListener#onTrainingEnd() onTrainingEnd()} is always called at the end of the training, even if the training was cancelled by a listener.</li>
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* </ul>
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*/
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public void train() {
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try {
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log.info("training starting.");
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getDataManager().writeInfo(this);
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boolean canContinue = notifyTrainingStarted();
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if (canContinue) {
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while (getStepCounter() < getConfiguration().getMaxStep()) {
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preEpoch();
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IDataManager.StatEntry statEntry = trainEpoch();
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postEpoch();
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incrementEpoch();
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if (getStepCounter() - lastSave >= Constants.MODEL_SAVE_FREQ) {
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getDataManager().save(this);
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lastSave = getStepCounter();
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canContinue = notifyEpochStarted();
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if (!canContinue) {
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break;
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}
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getDataManager().appendStat(statEntry);
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getDataManager().writeInfo(this);
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IDataManager.StatEntry statEntry = trainEpoch();
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postEpoch();
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canContinue = notifyEpochFinished(statEntry);
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if (!canContinue) {
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break;
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}
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log.info("Epoch: " + getEpochCounter() + ", reward: " + statEntry.getReward());
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incrementEpoch();
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}
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} catch (Exception e) {
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log.error("Training failed.", e);
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e.printStackTrace();
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}
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notifyTrainingFinished();
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}
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private boolean notifyTrainingStarted() {
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SyncTrainingEvent event = new SyncTrainingEvent(this);
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for (SyncTrainingListener listener : listeners) {
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if (listener.onTrainingStart(event) == SyncTrainingListener.ListenerResponse.STOP) {
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return false;
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}
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}
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return true;
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}
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private void notifyTrainingFinished() {
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for (SyncTrainingListener listener : listeners) {
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listener.onTrainingEnd();
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}
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}
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private boolean notifyEpochStarted() {
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SyncTrainingEvent event = new SyncTrainingEvent(this);
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for (SyncTrainingListener listener : listeners) {
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if (listener.onEpochStart(event) == SyncTrainingListener.ListenerResponse.STOP) {
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return false;
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}
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}
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return true;
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}
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private boolean notifyEpochFinished(IDataManager.StatEntry statEntry) {
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SyncTrainingEpochEndEvent event = new SyncTrainingEpochEndEvent(this, statEntry);
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for (SyncTrainingListener listener : listeners) {
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if (listener.onEpochEnd(event) == SyncTrainingListener.ListenerResponse.STOP) {
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return false;
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}
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}
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return true;
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}
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protected abstract void preEpoch();
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protected abstract void postEpoch();
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protected abstract IDataManager.StatEntry trainEpoch();
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protected abstract IDataManager.StatEntry trainEpoch(); // TODO: finish removal of IDataManager from Learning
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}
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@ -0,0 +1,22 @@
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package org.deeplearning4j.rl4j.learning.sync.listener;
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import lombok.Getter;
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import org.deeplearning4j.rl4j.learning.Learning;
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import org.deeplearning4j.rl4j.util.IDataManager;
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/**
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* A subclass of SyncTrainingEvent that is passed to SyncTrainingListener.onEpochEnd()
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*/
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public class SyncTrainingEpochEndEvent extends SyncTrainingEvent {
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/**
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* The stats of the epoch training
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*/
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@Getter
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private final IDataManager.StatEntry statEntry;
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public SyncTrainingEpochEndEvent(Learning learning, IDataManager.StatEntry statEntry) {
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super(learning);
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this.statEntry = statEntry;
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}
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}
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@ -0,0 +1,21 @@
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package org.deeplearning4j.rl4j.learning.sync.listener;
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import lombok.Getter;
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import lombok.Setter;
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import org.deeplearning4j.rl4j.learning.Learning;
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/**
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* SyncTrainingEvent are passed as parameters to the events of SyncTrainingListener
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*/
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public class SyncTrainingEvent {
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/**
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* The source of the event
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*/
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@Getter
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private final Learning learning;
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public SyncTrainingEvent(Learning learning) {
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this.learning = learning;
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}
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}
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@ -0,0 +1,45 @@
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package org.deeplearning4j.rl4j.learning.sync.listener;
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/**
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* A listener interface to use with a descendant of {@link org.deeplearning4j.rl4j.learning.sync.SyncLearning}
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*/
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public interface SyncTrainingListener {
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public enum ListenerResponse {
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/**
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* Tell SyncLearning to continue calling the listeners and the training.
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*/
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CONTINUE,
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/**
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* Tell SyncLearning to stop calling the listeners and terminate the training.
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*/
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STOP,
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}
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/**
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* Called once when the training starts.
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* @param event
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* @return A ListenerResponse telling the source of the event if it should go on or cancel the training.
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*/
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ListenerResponse onTrainingStart(SyncTrainingEvent event);
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/**
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* Called once when the training has finished. This method is called even when the training has been aborted.
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*/
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void onTrainingEnd();
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/**
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* Called before the start of every epoch.
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* @param event
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* @return A ListenerResponse telling the source of the event if it should continue or stop the training.
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*/
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ListenerResponse onEpochStart(SyncTrainingEvent event);
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/**
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* Called after the end of every epoch.
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* @param event
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* @return A ListenerResponse telling the source of the event if it should continue or stop the training.
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*/
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ListenerResponse onEpochEnd(SyncTrainingEpochEndEvent event);
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}
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@ -18,7 +18,6 @@ package org.deeplearning4j.rl4j.learning.sync.qlearning.discrete;
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import lombok.Getter;
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import lombok.Setter;
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import org.nd4j.linalg.primitives.Pair;
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import org.deeplearning4j.gym.StepReply;
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import org.deeplearning4j.rl4j.learning.Learning;
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import org.deeplearning4j.rl4j.learning.sync.Transition;
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@ -29,12 +28,13 @@ import org.deeplearning4j.rl4j.policy.DQNPolicy;
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import org.deeplearning4j.rl4j.policy.EpsGreedy;
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import org.deeplearning4j.rl4j.space.DiscreteSpace;
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import org.deeplearning4j.rl4j.space.Encodable;
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import org.deeplearning4j.rl4j.util.Constants;
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import org.deeplearning4j.rl4j.util.DataManagerSyncTrainingListener;
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import org.deeplearning4j.rl4j.util.IDataManager;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.factory.Nd4j;
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import org.nd4j.linalg.indexing.INDArrayIndex;
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import org.nd4j.linalg.indexing.NDArrayIndex;
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import org.nd4j.linalg.primitives.Pair;
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import org.nd4j.linalg.util.ArrayUtil;
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import java.util.ArrayList;
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@ -53,8 +53,6 @@ public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O
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@Getter
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final private QLConfiguration configuration;
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@Getter
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final private IDataManager dataManager;
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@Getter
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final private MDP<O, Integer, DiscreteSpace> mdp;
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@Getter
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final private IDQN currentDQN;
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@ -68,15 +66,23 @@ public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O
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private int lastAction;
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private INDArray history[] = null;
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private double accuReward = 0;
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private int lastMonitor = -Constants.MONITOR_FREQ;
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/**
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* @deprecated
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* Use QLearningDiscrete(MDP, IDQN, QLConfiguration, int) and add the required listeners with addListener() instead.
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*/
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@Deprecated
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public QLearningDiscrete(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, QLConfiguration conf,
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IDataManager dataManager, int epsilonNbStep) {
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this(mdp, dqn, conf, epsilonNbStep);
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addListener(DataManagerSyncTrainingListener.builder(dataManager).build());
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}
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public QLearningDiscrete(MDP<O, Integer, DiscreteSpace> mdp, IDQN dqn, QLConfiguration conf,
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int epsilonNbStep) {
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super(conf);
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this.configuration = conf;
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this.mdp = mdp;
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this.dataManager = dataManager;
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currentDQN = dqn;
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targetDQN = dqn.clone();
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policy = new DQNPolicy(getCurrentDQN());
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@ -85,7 +91,6 @@ public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O
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mdp.getActionSpace().setSeed(conf.getSeed());
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}
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public void postEpoch() {
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if (getHistoryProcessor() != null)
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@ -97,14 +102,6 @@ public abstract class QLearningDiscrete<O extends Encodable> extends QLearning<O
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history = null;
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lastAction = 0;
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accuReward = 0;
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if (getStepCounter() - lastMonitor >= Constants.MONITOR_FREQ && getHistoryProcessor() != null
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&& getDataManager().isSaveData()) {
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lastMonitor = getStepCounter();
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int[] shape = getMdp().getObservationSpace().getShape();
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getHistoryProcessor().startMonitor(getDataManager().getVideoDir() + "/video-" + getEpochCounter() + "-"
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+ getStepCounter() + ".mp4", shape);
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}
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}
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/**
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@ -0,0 +1,126 @@
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package org.deeplearning4j.rl4j.util;
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import lombok.extern.slf4j.Slf4j;
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import org.deeplearning4j.rl4j.learning.sync.listener.SyncTrainingEpochEndEvent;
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import org.deeplearning4j.rl4j.learning.sync.listener.SyncTrainingEvent;
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import org.deeplearning4j.rl4j.learning.sync.listener.SyncTrainingListener;
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/**
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* DataManagerSyncTrainingListener can be added to the listeners of SyncLearning so that the
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* training process can be fed to the DataManager
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*/
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@Slf4j
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public class DataManagerSyncTrainingListener implements SyncTrainingListener {
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private final IDataManager dataManager;
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private final int saveFrequency;
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private final int monitorFrequency;
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private int lastSave;
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private int lastMonitor;
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private DataManagerSyncTrainingListener(Builder builder) {
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this.dataManager = builder.dataManager;
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this.saveFrequency = builder.saveFrequency;
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this.lastSave = -builder.saveFrequency;
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this.monitorFrequency = builder.monitorFrequency;
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this.lastMonitor = -builder.monitorFrequency;
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}
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@Override
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public ListenerResponse onTrainingStart(SyncTrainingEvent event) {
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try {
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dataManager.writeInfo(event.getLearning());
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} catch (Exception e) {
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log.error("Training failed.", e);
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return ListenerResponse.STOP;
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}
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return ListenerResponse.CONTINUE;
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}
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@Override
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public void onTrainingEnd() {
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// Do nothing
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}
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@Override
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public ListenerResponse onEpochStart(SyncTrainingEvent event) {
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int stepCounter = event.getLearning().getStepCounter();
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if (stepCounter - lastMonitor >= monitorFrequency
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&& event.getLearning().getHistoryProcessor() != null
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&& dataManager.isSaveData()) {
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lastMonitor = stepCounter;
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int[] shape = event.getLearning().getMdp().getObservationSpace().getShape();
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event.getLearning().getHistoryProcessor().startMonitor(dataManager.getVideoDir() + "/video-" + event.getLearning().getEpochCounter() + "-"
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+ stepCounter + ".mp4", shape);
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}
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return ListenerResponse.CONTINUE;
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}
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@Override
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public ListenerResponse onEpochEnd(SyncTrainingEpochEndEvent event) {
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try {
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int stepCounter = event.getLearning().getStepCounter();
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if (stepCounter - lastSave >= saveFrequency) {
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dataManager.save(event.getLearning());
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lastSave = stepCounter;
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}
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dataManager.appendStat(event.getStatEntry());
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dataManager.writeInfo(event.getLearning());
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} catch (Exception e) {
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log.error("Training failed.", e);
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return ListenerResponse.STOP;
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}
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return ListenerResponse.CONTINUE;
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}
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public static Builder builder(IDataManager dataManager) {
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return new Builder(dataManager);
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}
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public static class Builder {
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private final IDataManager dataManager;
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private int saveFrequency = Constants.MODEL_SAVE_FREQ;
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private int monitorFrequency = Constants.MONITOR_FREQ;
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/**
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* Create a Builder with the given DataManager
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* @param dataManager
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*/
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public Builder(IDataManager dataManager) {
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this.dataManager = dataManager;
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}
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/**
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* A number that represent the number of steps since the last call to DataManager.save() before can it be called again.
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* @param saveFrequency (Default: 100000)
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*/
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public Builder saveFrequency(int saveFrequency) {
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this.saveFrequency = saveFrequency;
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return this;
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}
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/**
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* A number that represent the number of steps since the last call to HistoryProcessor.startMonitor() before can it be called again.
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* @param monitorFrequency (Default: 10000)
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*/
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public Builder monitorFrequency(int monitorFrequency) {
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this.monitorFrequency = monitorFrequency;
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return this;
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}
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||||
|
||||
/**
|
||||
* Creates a DataManagerSyncTrainingListener with the configured parameters
|
||||
* @return An instance of DataManagerSyncTrainingListener
|
||||
*/
|
||||
public DataManagerSyncTrainingListener build() {
|
||||
return new DataManagerSyncTrainingListener(this);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
|
@ -1,12 +1,13 @@
|
|||
package org.deeplearning4j.rl4j.learning.sync;
|
||||
|
||||
import lombok.AllArgsConstructor;
|
||||
import lombok.Value;
|
||||
import org.deeplearning4j.rl4j.learning.ILearning;
|
||||
import org.deeplearning4j.rl4j.learning.sync.qlearning.QLearning;
|
||||
import org.deeplearning4j.rl4j.learning.sync.support.MockStatEntry;
|
||||
import org.deeplearning4j.rl4j.learning.sync.support.MockSyncTrainingListener;
|
||||
import org.deeplearning4j.rl4j.mdp.MDP;
|
||||
import org.deeplearning4j.rl4j.network.NeuralNet;
|
||||
import org.deeplearning4j.rl4j.policy.Policy;
|
||||
import org.deeplearning4j.rl4j.support.MockDataManager;
|
||||
import org.deeplearning4j.rl4j.util.DataManagerSyncTrainingListener;
|
||||
import org.deeplearning4j.rl4j.util.IDataManager;
|
||||
import org.junit.Test;
|
||||
|
||||
|
|
@ -15,61 +16,101 @@ import static org.junit.Assert.assertEquals;
|
|||
public class SyncLearningTest {
|
||||
|
||||
@Test
|
||||
public void refac_checkDataManagerCallsRemainTheSame() {
|
||||
public void when_training_expect_listenersToBeCalled() {
|
||||
// Arrange
|
||||
MockLConfiguration lconfig = new MockLConfiguration(10);
|
||||
MockDataManager dataManager = new MockDataManager(false);
|
||||
MockSyncLearning sut = new MockSyncLearning(lconfig, dataManager, 2);
|
||||
QLearning.QLConfiguration lconfig = QLearning.QLConfiguration.builder().maxStep(10).build();
|
||||
MockSyncTrainingListener listener = new MockSyncTrainingListener();
|
||||
MockSyncLearning sut = new MockSyncLearning(lconfig);
|
||||
sut.addListener(listener);
|
||||
|
||||
// Act
|
||||
sut.train();
|
||||
|
||||
assertEquals(10, dataManager.statEntries.size());
|
||||
for(int i = 0; i < 10; ++i) {
|
||||
IDataManager.StatEntry entry = dataManager.statEntries.get(i);
|
||||
assertEquals(2, entry.getEpochCounter());
|
||||
assertEquals(i+1, entry.getStepCounter());
|
||||
assertEquals(1.0, entry.getReward(), 0.0);
|
||||
|
||||
assertEquals(1, listener.onTrainingStartCallCount);
|
||||
assertEquals(10, listener.onEpochStartCallCount);
|
||||
assertEquals(10, listener.onEpochEndStartCallCount);
|
||||
assertEquals(1, listener.onTrainingEndCallCount);
|
||||
}
|
||||
assertEquals(0, dataManager.isSaveDataCallCount);
|
||||
assertEquals(0, dataManager.getVideoDirCallCount);
|
||||
assertEquals(11, dataManager.writeInfoCallCount);
|
||||
assertEquals(1, dataManager.saveCallCount);
|
||||
|
||||
@Test
|
||||
public void when_trainingStartCanContinueFalse_expect_trainingStopped() {
|
||||
// Arrange
|
||||
QLearning.QLConfiguration lconfig = QLearning.QLConfiguration.builder().maxStep(10).build();
|
||||
MockSyncTrainingListener listener = new MockSyncTrainingListener();
|
||||
MockSyncLearning sut = new MockSyncLearning(lconfig);
|
||||
sut.addListener(listener);
|
||||
listener.trainingStartCanContinue = false;
|
||||
|
||||
// Act
|
||||
sut.train();
|
||||
|
||||
assertEquals(1, listener.onTrainingStartCallCount);
|
||||
assertEquals(0, listener.onEpochStartCallCount);
|
||||
assertEquals(0, listener.onEpochEndStartCallCount);
|
||||
assertEquals(1, listener.onTrainingEndCallCount);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_epochStartCanContinueFalse_expect_trainingStopped() {
|
||||
// Arrange
|
||||
QLearning.QLConfiguration lconfig = QLearning.QLConfiguration.builder().maxStep(10).build();
|
||||
MockSyncTrainingListener listener = new MockSyncTrainingListener();
|
||||
MockSyncLearning sut = new MockSyncLearning(lconfig);
|
||||
sut.addListener(listener);
|
||||
listener.nbStepsEpochStartCanContinue = 3;
|
||||
|
||||
// Act
|
||||
sut.train();
|
||||
|
||||
assertEquals(1, listener.onTrainingStartCallCount);
|
||||
assertEquals(3, listener.onEpochStartCallCount);
|
||||
assertEquals(2, listener.onEpochEndStartCallCount);
|
||||
assertEquals(1, listener.onTrainingEndCallCount);
|
||||
}
|
||||
|
||||
@Test
|
||||
public void when_epochEndCanContinueFalse_expect_trainingStopped() {
|
||||
// Arrange
|
||||
QLearning.QLConfiguration lconfig = QLearning.QLConfiguration.builder().maxStep(10).build();
|
||||
MockSyncTrainingListener listener = new MockSyncTrainingListener();
|
||||
MockSyncLearning sut = new MockSyncLearning(lconfig);
|
||||
sut.addListener(listener);
|
||||
listener.nbStepsEpochEndCanContinue = 3;
|
||||
|
||||
// Act
|
||||
sut.train();
|
||||
|
||||
assertEquals(1, listener.onTrainingStartCallCount);
|
||||
assertEquals(3, listener.onEpochStartCallCount);
|
||||
assertEquals(3, listener.onEpochEndStartCallCount);
|
||||
assertEquals(1, listener.onTrainingEndCallCount);
|
||||
}
|
||||
|
||||
public static class MockSyncLearning extends SyncLearning {
|
||||
|
||||
private final IDataManager dataManager;
|
||||
private LConfiguration conf;
|
||||
private final int epochSteps;
|
||||
|
||||
public MockSyncLearning(LConfiguration conf, IDataManager dataManager, int epochSteps) {
|
||||
public MockSyncLearning(LConfiguration conf, IDataManager dataManager) {
|
||||
super(conf);
|
||||
addListener(DataManagerSyncTrainingListener.builder(dataManager).build());
|
||||
this.conf = conf;
|
||||
}
|
||||
|
||||
public MockSyncLearning(LConfiguration conf) {
|
||||
super(conf);
|
||||
this.dataManager = dataManager;
|
||||
this.conf = conf;
|
||||
this.epochSteps = epochSteps;
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void preEpoch() {
|
||||
|
||||
}
|
||||
protected void preEpoch() { }
|
||||
|
||||
@Override
|
||||
protected void postEpoch() {
|
||||
|
||||
}
|
||||
protected void postEpoch() { }
|
||||
|
||||
@Override
|
||||
protected IDataManager.StatEntry trainEpoch() {
|
||||
setStepCounter(getStepCounter() + 1);
|
||||
return new MockStatEntry(epochSteps, getStepCounter(), 1.0);
|
||||
}
|
||||
|
||||
@Override
|
||||
protected IDataManager getDataManager() {
|
||||
return dataManager;
|
||||
return new MockStatEntry(getEpochCounter(), getStepCounter(), 1.0);
|
||||
}
|
||||
|
||||
@Override
|
||||
|
|
@ -92,41 +133,4 @@ public class SyncLearningTest {
|
|||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
public static class MockLConfiguration implements ILearning.LConfiguration {
|
||||
|
||||
private final int maxStep;
|
||||
|
||||
public MockLConfiguration(int maxStep) {
|
||||
this.maxStep = maxStep;
|
||||
}
|
||||
|
||||
@Override
|
||||
public int getSeed() {
|
||||
return 0;
|
||||
}
|
||||
|
||||
@Override
|
||||
public int getMaxEpochStep() {
|
||||
return 0;
|
||||
}
|
||||
|
||||
@Override
|
||||
public int getMaxStep() {
|
||||
return maxStep;
|
||||
}
|
||||
|
||||
@Override
|
||||
public double getGamma() {
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
@AllArgsConstructor
|
||||
@Value
|
||||
public static class MockStatEntry implements IDataManager.StatEntry {
|
||||
int epochCounter;
|
||||
int stepCounter;
|
||||
double reward;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,65 @@
|
|||
package org.deeplearning4j.rl4j.learning.sync.qlearning;
|
||||
|
||||
import org.deeplearning4j.rl4j.learning.IHistoryProcessor;
|
||||
import org.deeplearning4j.rl4j.learning.sync.qlearning.discrete.QLearningDiscrete;
|
||||
import org.deeplearning4j.rl4j.learning.sync.support.MockDQN;
|
||||
import org.deeplearning4j.rl4j.learning.sync.support.MockMDP;
|
||||
import org.deeplearning4j.rl4j.learning.sync.support.MockStatEntry;
|
||||
import org.deeplearning4j.rl4j.mdp.MDP;
|
||||
import org.deeplearning4j.rl4j.support.MockDataManager;
|
||||
import org.deeplearning4j.rl4j.support.MockHistoryProcessor;
|
||||
import org.deeplearning4j.rl4j.util.DataManagerSyncTrainingListener;
|
||||
import org.deeplearning4j.rl4j.util.IDataManager;
|
||||
import org.junit.Test;
|
||||
|
||||
import static org.junit.Assert.assertEquals;
|
||||
|
||||
public class QLearningDiscreteTest {
|
||||
@Test
|
||||
public void refac_checkDataManagerCallsRemainTheSame() {
|
||||
// Arrange
|
||||
QLearning.QLConfiguration lconfig = QLearning.QLConfiguration.builder()
|
||||
.maxStep(10)
|
||||
.expRepMaxSize(1)
|
||||
.build();
|
||||
MockDataManager dataManager = new MockDataManager(true);
|
||||
MockQLearningDiscrete sut = new MockQLearningDiscrete(10, lconfig, dataManager, 2, 3);
|
||||
IHistoryProcessor.Configuration hpConfig = IHistoryProcessor.Configuration.builder()
|
||||
.build();
|
||||
sut.setHistoryProcessor(new MockHistoryProcessor(hpConfig));
|
||||
|
||||
// Act
|
||||
sut.train();
|
||||
|
||||
assertEquals(10, dataManager.statEntries.size());
|
||||
for(int i = 0; i < 10; ++i) {
|
||||
IDataManager.StatEntry entry = dataManager.statEntries.get(i);
|
||||
assertEquals(i, entry.getEpochCounter());
|
||||
assertEquals(i+1, entry.getStepCounter());
|
||||
assertEquals(1.0, entry.getReward(), 0.0);
|
||||
|
||||
}
|
||||
assertEquals(4, dataManager.isSaveDataCallCount);
|
||||
assertEquals(4, dataManager.getVideoDirCallCount);
|
||||
assertEquals(11, dataManager.writeInfoCallCount);
|
||||
assertEquals(5, dataManager.saveCallCount);
|
||||
}
|
||||
|
||||
public static class MockQLearningDiscrete extends QLearningDiscrete {
|
||||
|
||||
public MockQLearningDiscrete(int maxSteps, QLConfiguration conf,
|
||||
IDataManager dataManager, int saveFrequency, int monitorFrequency) {
|
||||
super(new MockMDP(maxSteps), new MockDQN(), conf, 2);
|
||||
addListener(DataManagerSyncTrainingListener.builder(dataManager)
|
||||
.saveFrequency(saveFrequency)
|
||||
.monitorFrequency(monitorFrequency)
|
||||
.build());
|
||||
}
|
||||
|
||||
@Override
|
||||
protected IDataManager.StatEntry trainEpoch() {
|
||||
setStepCounter(getStepCounter() + 1);
|
||||
return new MockStatEntry(getEpochCounter(), getStepCounter(), 1.0);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,92 @@
|
|||
package org.deeplearning4j.rl4j.learning.sync.support;
|
||||
|
||||
import org.deeplearning4j.nn.api.NeuralNetwork;
|
||||
import org.deeplearning4j.nn.gradient.Gradient;
|
||||
import org.deeplearning4j.rl4j.network.NeuralNet;
|
||||
import org.deeplearning4j.rl4j.network.dqn.IDQN;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
|
||||
import java.io.IOException;
|
||||
import java.io.OutputStream;
|
||||
|
||||
public class MockDQN implements IDQN {
|
||||
@Override
|
||||
public NeuralNetwork[] getNeuralNetworks() {
|
||||
return new NeuralNetwork[0];
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean isRecurrent() {
|
||||
return false;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void reset() {
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public void fit(INDArray input, INDArray labels) {
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public void fit(INDArray input, INDArray[] labels) {
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public INDArray output(INDArray batch) {
|
||||
return null;
|
||||
}
|
||||
|
||||
@Override
|
||||
public INDArray[] outputAll(INDArray batch) {
|
||||
return new INDArray[0];
|
||||
}
|
||||
|
||||
@Override
|
||||
public IDQN clone() {
|
||||
return null;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void copy(NeuralNet from) {
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public void copy(IDQN from) {
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public Gradient[] gradient(INDArray input, INDArray label) {
|
||||
return new Gradient[0];
|
||||
}
|
||||
|
||||
@Override
|
||||
public Gradient[] gradient(INDArray input, INDArray[] label) {
|
||||
return new Gradient[0];
|
||||
}
|
||||
|
||||
@Override
|
||||
public void applyGradient(Gradient[] gradient, int batchSize) {
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public double getLatestScore() {
|
||||
return 0;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void save(OutputStream os) throws IOException {
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public void save(String filename) throws IOException {
|
||||
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,79 @@
|
|||
package org.deeplearning4j.rl4j.learning.sync.support;
|
||||
|
||||
import org.deeplearning4j.gym.StepReply;
|
||||
import org.deeplearning4j.rl4j.mdp.MDP;
|
||||
import org.deeplearning4j.rl4j.space.DiscreteSpace;
|
||||
import org.deeplearning4j.rl4j.space.ObservationSpace;
|
||||
import org.nd4j.linalg.api.ndarray.INDArray;
|
||||
|
||||
public class MockMDP implements MDP<Object, Integer, DiscreteSpace> {
|
||||
|
||||
private final int maxSteps;
|
||||
private final DiscreteSpace actionSpace = new DiscreteSpace(1);
|
||||
private final MockObservationSpace observationSpace = new MockObservationSpace();
|
||||
|
||||
private int currentStep = 0;
|
||||
|
||||
public MockMDP(int maxSteps) {
|
||||
|
||||
this.maxSteps = maxSteps;
|
||||
}
|
||||
|
||||
@Override
|
||||
public ObservationSpace<Object> getObservationSpace() {
|
||||
return observationSpace;
|
||||
}
|
||||
|
||||
@Override
|
||||
public DiscreteSpace getActionSpace() {
|
||||
return actionSpace;
|
||||
}
|
||||
|
||||
@Override
|
||||
public Object reset() {
|
||||
return null;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void close() {
|
||||
|
||||
}
|
||||
|
||||
@Override
|
||||
public StepReply<Object> step(Integer integer) {
|
||||
return new StepReply<Object>(null, 1.0, isDone(), null);
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean isDone() {
|
||||
return currentStep >= maxSteps;
|
||||
}
|
||||
|
||||
@Override
|
||||
public MDP<Object, Integer, DiscreteSpace> newInstance() {
|
||||
return null;
|
||||
}
|
||||
|
||||
private static class MockObservationSpace implements ObservationSpace {
|
||||
|
||||
@Override
|
||||
public String getName() {
|
||||
return null;
|
||||
}
|
||||
|
||||
@Override
|
||||
public int[] getShape() {
|
||||
return new int[0];
|
||||
}
|
||||
|
||||
@Override
|
||||
public INDArray getLow() {
|
||||
return null;
|
||||
}
|
||||
|
||||
@Override
|
||||
public INDArray getHigh() {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
package org.deeplearning4j.rl4j.learning.sync.support;
|
||||
|
||||
import lombok.AllArgsConstructor;
|
||||
import lombok.Value;
|
||||
import org.deeplearning4j.rl4j.util.IDataManager;
|
||||
|
||||
@AllArgsConstructor
|
||||
@Value
|
||||
public class MockStatEntry implements IDataManager.StatEntry {
|
||||
int epochCounter;
|
||||
int stepCounter;
|
||||
double reward;
|
||||
}
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
package org.deeplearning4j.rl4j.learning.sync.support;
|
||||
|
||||
import org.deeplearning4j.rl4j.learning.sync.listener.SyncTrainingEpochEndEvent;
|
||||
import org.deeplearning4j.rl4j.learning.sync.listener.SyncTrainingEvent;
|
||||
import org.deeplearning4j.rl4j.learning.sync.listener.SyncTrainingListener;
|
||||
|
||||
public class MockSyncTrainingListener implements SyncTrainingListener {
|
||||
|
||||
public int onTrainingStartCallCount = 0;
|
||||
public int onTrainingEndCallCount = 0;
|
||||
public int onEpochStartCallCount = 0;
|
||||
public int onEpochEndStartCallCount = 0;
|
||||
|
||||
public boolean trainingStartCanContinue = true;
|
||||
public int nbStepsEpochStartCanContinue = Integer.MAX_VALUE;
|
||||
public int nbStepsEpochEndCanContinue = Integer.MAX_VALUE;
|
||||
|
||||
@Override
|
||||
public ListenerResponse onTrainingStart(SyncTrainingEvent event) {
|
||||
++onTrainingStartCallCount;
|
||||
return trainingStartCanContinue ? ListenerResponse.CONTINUE : ListenerResponse.STOP;
|
||||
}
|
||||
|
||||
@Override
|
||||
public void onTrainingEnd() {
|
||||
++onTrainingEndCallCount;
|
||||
}
|
||||
|
||||
@Override
|
||||
public ListenerResponse onEpochStart(SyncTrainingEvent event) {
|
||||
++onEpochStartCallCount;
|
||||
if(onEpochStartCallCount >= nbStepsEpochStartCanContinue) {
|
||||
return ListenerResponse.STOP;
|
||||
}
|
||||
return ListenerResponse.CONTINUE;
|
||||
}
|
||||
|
||||
@Override
|
||||
public ListenerResponse onEpochEnd(SyncTrainingEpochEndEvent event) {
|
||||
++onEpochEndStartCallCount;
|
||||
if(onEpochEndStartCallCount >= nbStepsEpochEndCanContinue) {
|
||||
return ListenerResponse.STOP;
|
||||
}
|
||||
return ListenerResponse.CONTINUE;
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue