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RL4J: Replace gym-java-client with JavaCPP (#8595) 

* RL4J: Replace gym-java-client with JavaCPP

Signed-off-by: Samuel Audet <samuel.audet@gmail.com>
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Samuel Audet 2020-01-20 17:13:57 +09:00 committed by GitHub
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README.md
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@ -7,7 +7,6 @@ Welcome to the new monorepo of Deeplearning4j that contains the source code for
* https://github.com/eclipse/deeplearning4j/tree/master/datavec
* https://github.com/eclipse/deeplearning4j/tree/master/arbiter
* https://github.com/eclipse/deeplearning4j/tree/master/nd4s
* https://github.com/eclipse/deeplearning4j/tree/master/gym-java-client
* https://github.com/eclipse/deeplearning4j/tree/master/rl4j
* https://github.com/eclipse/deeplearning4j/tree/master/scalnet
* https://github.com/eclipse/deeplearning4j/tree/master/pydl4j

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gym-java-client/.gitignore vendored
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target/
.idea/
*.iml
*-git.properties

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gym-java-client/LICENSE.txt
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gym-java-client/README.md
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# gym-java-client
A java http client for [gym-http-api](https://github.com/openai/gym-http-api).
Note: If you are encountering errors as reported in [issue #13](https://github.com/deeplearning4j/gym-java-client/issues/13), please execute the following command before launching `python gym_http_server.py`:
```bash
$ sudo sysctl -w net.ipv4.tcp_tw_recycle=1
```
# Quickstart
To create a new Client, use the ClientFactory. If the url is not localhost:5000, provide it as a second argument
```java
Client<Box, Integer, DiscreteSpace> client = ClientFactory.build("CartPole-v0");
```
"CartPole-v0" is the name of the gym environment.
The type parameters of a client are the Observation type, the Action type, the Observation Space type and the ActionSpace type.
It is a bit cumbersome to both declare an ActionSpace and an Action since an ActionSpace knows what type is an Action but unfortunately java does't support type member and path dependant types.
Here we use Box and BoxSpace for the environment and Integer and Discrete Space because it is how [CartPole-v0](https://gym.openai.com/envs/CartPole-v0) is specified.
The methods nomenclature follows closely the api interface of [gym-http-api](https://github.com/openai/gym-http-api#api-specification), O is Observation an A is Action:
```java
//Static methods
/**
* @param url url of the server
* @return set of all environments running on the server at the url
*/
public static Set<String> listAll(String url);
/**
* Shutdown the server at the url
*
* @param url url of the server
*/
public static void serverShutdown(String url);
//Methods accessible from a Client
/**
* @return set of all environments running on the same server than this client
*/
public Set<String> listAll();
/**
* Step the environment by one action
*
* @param action action to step the environment with
* @return the StepReply containing the next observation, the reward, if it is a terminal state and optional information.
*/
public StepReply<O> step(A action);
/**
* Reset the state of the environment and return an initial observation.
*
* @return initial observation
*/
public O reset();
/**
* Start monitoring.
*
* @param directory path to directory in which store the monitoring file
* @param force clear out existing training data from this directory (by deleting every file prefixed with "openaigym.")
* @param resume retain the training data already in this directory, which will be merged with our new data
*/
public void monitorStart(String directory, boolean force, boolean resume);
/**
* Flush all monitor data to disk
*/
public void monitorClose();
/**
* Upload monitoring data to OpenAI servers.
*
* @param trainingDir directory that contains the monitoring data
* @param apiKey personal OpenAI API key
* @param algorithmId an arbitrary string indicating the paricular version of the algorithm (including choices of parameters) you are running.
**/
public void upload(String trainingDir, String apiKey, String algorithmId);
/**
* Upload monitoring data to OpenAI servers.
*
* @param trainingDir directory that contains the monitoring data
* @param apiKey personal OpenAI API key
*/
public void upload(String trainingDir, String apiKey);
/**
* Shutdown the server at the same url than this client
*/
public void serverShutdown()
```
## TODO
* Add all ObservationSpace and ActionSpace when they will be available.

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gym-java-client/contrib/formatter.xml
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~ Copyright (c) 2015-2018 Skymind, Inc.
~
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~ Unless required by applicable law or agreed to in writing, software
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</profile>
</profiles>

337
gym-java-client/pom.xml
View File

@ -1,337 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<!--~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
~ Copyright (c) 2015-2018 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
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-->
<project xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
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">
<parent>
<groupId>org.deeplearning4j</groupId>
<artifactId>deeplearning4j</artifactId>
<version>1.0.0-SNAPSHOT</version>
</parent>
<modelVersion>4.0.0</modelVersion>
<groupId>org.deeplearning4j</groupId>
<artifactId>gym-java-client</artifactId>
<name>gym-java-client</name>
<description>A Java client for Open AI's Reinforcement Learning Gym</description>
<licenses>
<license>
<name>Apache License, Version 2.0</name>
<url>http://www.apache.org/licenses/LICENSE-2.0.txt</url>
<distribution>repo</distribution>
</license>
</licenses>
<developers>
<developer>
<id>rubenfiszel</id>
<name>Ruben Fiszel</name>
<email>ruben.fiszel@epfl.ch</email>
</developer>
</developers>
<properties>
<nd4j.backend>nd4j-native</nd4j.backend>
</properties>
<dependencies>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>${nd4j.backend}</artifactId>
<version>${nd4j.version}</version>
</dependency>
<dependency>
<groupId>commons-codec</groupId>
<artifactId>commons-codec</artifactId>
<version>${commons-codec.version}</version>
</dependency>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpclient</artifactId>
<version>${httpclient.version}</version>
</dependency>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpcore</artifactId>
<version>${httpcore.version}</version>
</dependency>
<dependency>
<groupId>org.apache.httpcomponents</groupId>
<artifactId>httpmime</artifactId>
<version>${httpmime.version}</version>
</dependency>
<dependency>
<groupId>com.mashape.unirest</groupId>
<artifactId>unirest-java</artifactId>
<version>${unirest.version}</version>
</dependency>
<dependency>
<groupId>org.objenesis</groupId>
<artifactId>objenesis</artifactId>
<version>${objenesis.version}</version>
</dependency>
<dependency>
<groupId>org.mockito</groupId>
<artifactId>mockito-core</artifactId>
<version>${mockito.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.ow2.asm</groupId>
<artifactId>asm</artifactId>
<version>${asm.version}</version>
</dependency>
<dependency>
<groupId>cglib</groupId>
<artifactId>cglib</artifactId>
<version>3.1</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>${junit.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.powermock</groupId>
<artifactId>powermock-api-mockito2</artifactId>
<version>1.7.3</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.powermock</groupId>
<artifactId>powermock-module-junit4</artifactId>
<version>1.7.3</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-api</artifactId>
<version>${slf4j.version}</version>
</dependency>
<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-classic</artifactId>
<version>${logback.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-core</artifactId>
<version>${logback.version}</version>
<scope>test</scope>
</dependency>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<version>${lombok.version}</version>
<scope>provided</scope>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<artifactId>maven-source-plugin</artifactId>
<version>${maven-source-plugin.version}</version>
<executions>
<execution>
<id>attach-sources</id>
<goals>
<goal>jar</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<artifactId>maven-surefire-plugin</artifactId>
<version>${maven-surefire-plugin.version}</version>
<configuration>
<!--
By default: Surefire will set the classpath based on the manifest. Because tests are not included
in the JAR, any tests that rely on class path scanning for resources in the tests directory will not
function correctly without this configuratino.
For example, tests for custom layers (where the custom layer is defined in the test directory)
will fail due to the custom layer not being found on the classpath.
http://maven.apache.org/surefire/maven-surefire-plugin/examples/class-loading.html
-->
<useSystemClassLoader>true</useSystemClassLoader>
<useManifestOnlyJar>false</useManifestOnlyJar>
</configuration>
</plugin>
<plugin>
<artifactId>maven-javadoc-plugin</artifactId>
<version>${maven-javadoc-plugin.version}</version>
<configuration>
<additionalparam>-Xdoclint:none</additionalparam>
</configuration>
<executions>
<execution>
<id>attach-javadocs</id>
<goals>
<goal>jar</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>com.lewisd</groupId>
<artifactId>lint-maven-plugin</artifactId>
<version>0.0.11</version>
<configuration>
<failOnViolation>true</failOnViolation>
<onlyRunRules>
<rule>DuplicateDep</rule>
<rule>RedundantDepVersion</rule>
<rule>RedundantPluginVersion</rule>
<!-- Rules incompatible with Java 9
<rule>VersionProp</rule>
<rule>DotVersionProperty</rule> -->
</onlyRunRules>
</configuration>
<executions>
<execution>
<id>pom-lint</id>
<phase>validate</phase>
<goals>
<goal>check</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>net.revelc.code.formatter</groupId>
<artifactId>formatter-maven-plugin</artifactId>
<version>2.0.0</version>
<configuration>
<configFile>${session.executionRootDirectory}/contrib/formatter.xml</configFile>
</configuration>
</plugin>
<!-- Configuration for git-commit-id plugin - used with ND4J version check functionality -->
<plugin>
<groupId>pl.project13.maven</groupId>
<artifactId>git-commit-id-plugin</artifactId>
<version>${maven-git-commit-plugin.version}</version>
<executions>
<execution>
<goals>
<goal>revision</goal>
</goals>
<phase>generate-resources</phase>
</execution>
</executions>
<configuration>
<generateGitPropertiesFile>true</generateGitPropertiesFile>
<generateGitPropertiesFilename>
${project.basedir}/target/generated-sources/src/main/resources/ai/skymind/${project.groupId}-${project.artifactId}-git.properties
</generateGitPropertiesFilename>
<gitDescribe>
<skip>true</skip>
</gitDescribe>
</configuration>
</plugin>
<!-- Add generated git.properties files resource directory, for output of git-commit-id plugin -->
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>build-helper-maven-plugin</artifactId>
<version>${maven-build-helper-plugin.version}</version>
<executions>
<execution>
<id>add-resource</id>
<phase>generate-resources</phase>
<goals>
<goal>add-resource</goal>
</goals>
<configuration>
<resources>
<resource>
<directory>
${project.basedir}/target/generated-sources/src/main/resources
</directory>
</resource>
</resources>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
<pluginManagement>
<plugins>
<plugin>
<groupId>org.eclipse.m2e</groupId>
<artifactId>lifecycle-mapping</artifactId>
<version>1.0.0</version>
<configuration>
<lifecycleMappingMetadata>
<pluginExecutions>
<pluginExecution>
<pluginExecutionFilter>
<groupId>com.lewisd</groupId>
<artifactId>lint-maven-plugin</artifactId>
<versionRange>[0.0.11,)</versionRange>
<goals>
<goal>check</goal>
</goals>
</pluginExecutionFilter>
<action>
<ignore/>
</action>
</pluginExecution>
</pluginExecutions>
</lifecycleMappingMetadata>
</configuration>
</plugin>
</plugins>
</pluginManagement>
</build>
<reporting>
<plugins>
<plugin>
<artifactId>maven-surefire-report-plugin</artifactId>
<version>2.19.1</version>
</plugin>
<!-- Test coverage -->
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>cobertura-maven-plugin</artifactId>
<version>2.7</version>
</plugin>
</plugins>
</reporting>
<profiles>
<profile>
<id>nd4j-backend</id>
<activation>
<property>
<name>libnd4j.cuda</name>
</property>
</activation>
<properties>
<nd4j.backend>nd4j-cuda-${libnd4j.cuda}</nd4j.backend>
</properties>
</profile>
</profiles>
</project>

200
gym-java-client/src/main/java/org/deeplearning4j/gym/Client.java
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@ -1,200 +0,0 @@
/*******************************************************************************
* Copyright (c) 2015-2018 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.gym;
import com.mashape.unirest.http.JsonNode;
import lombok.Value;
import lombok.extern.slf4j.Slf4j;
import org.deeplearning4j.rl4j.space.GymObservationSpace;
import org.deeplearning4j.rl4j.space.ActionSpace;
import org.json.JSONObject;
import java.util.Set;
/**
* @author rubenfiszel (ruben.fiszel@epfl.ch) on 7/6/16.
*
* A client represent an active connection to a specific instance of an environment on a rl4j-http-api server.
* for API specification
*
* @param <O> Observation type
* @param <A> Action type
* @param <AS> Action Space type
* @see <a href="https://github.com/openai/gym-http-api#api-specification">https://github.com/openai/gym-http-api#api-specification</a>
*/
@Slf4j
@Value
public class Client<O, A, AS extends ActionSpace<A>> {
public static String V1_ROOT = "/v1";
public static String ENVS_ROOT = V1_ROOT + "/envs/";
public static String MONITOR_START = "/monitor/start/";
public static String MONITOR_CLOSE = "/monitor/close/";
public static String CLOSE = "/close/";
public static String RESET = "/reset/";
public static String SHUTDOWN = "/shutdown/";
public static String UPLOAD = "/upload/";
public static String STEP = "/step/";
public static String OBSERVATION_SPACE = "/observation_space/";
public static String ACTION_SPACE = "/action_space/";
String url;
String envId;
String instanceId;
GymObservationSpace<O> observationSpace;
AS actionSpace;
boolean render;
/**
* @param url url of the server
* @return set of all environments running on the server at the url
*/
public static Set<String> listAll(String url) {
JSONObject reply = ClientUtils.get(url + ENVS_ROOT);
return reply.getJSONObject("envs").keySet();
}
/**
* Shutdown the server at the url
*
* @param url url of the server
*/
public static void serverShutdown(String url) {
ClientUtils.post(url + ENVS_ROOT + SHUTDOWN, new JSONObject());
}
/**
* @return set of all environments running on the same server than this client
*/
public Set<String> listAll() {
return listAll(url);
}
/**
* Step the environment by one action
*
* @param action action to step the environment with
* @return the StepReply containing the next observation, the reward, if it is a terminal state and optional information.
*/
public StepReply<O> step(A action) {
JSONObject body = new JSONObject().put("action", getActionSpace().encode(action)).put("render", render);
JSONObject reply = ClientUtils.post(url + ENVS_ROOT + instanceId + STEP, body).getObject();
O observation = observationSpace.getValue(reply, "observation");
double reward = reply.getDouble("reward");
boolean done = reply.getBoolean("done");
JSONObject info = reply.getJSONObject("info");
return new StepReply<O>(observation, reward, done, info);
}
/**
* Reset the state of the environment and return an initial observation.
*
* @return initial observation
*/
public O reset() {
JsonNode resetRep = ClientUtils.post(url + ENVS_ROOT + instanceId + RESET, new JSONObject());
return observationSpace.getValue(resetRep.getObject(), "observation");
}
/*
Present in the doc but not working currently server-side
public void monitorStart(String directory) {
JSONObject json = new JSONObject()
.put("directory", directory);
monitorStartPost(json);
}
*/
/**
* Start monitoring.
*
* @param directory path to directory in which store the monitoring file
* @param force clear out existing training data from this directory (by deleting every file prefixed with "openaigym.")
* @param resume retain the training data already in this directory, which will be merged with our new data
*/
public void monitorStart(String directory, boolean force, boolean resume) {
JSONObject json = new JSONObject().put("directory", directory).put("force", force).put("resume", resume);
monitorStartPost(json);
}
private void monitorStartPost(JSONObject json) {
ClientUtils.post(url + ENVS_ROOT + instanceId + MONITOR_START, json);
}
/**
* Flush all monitor data to disk
*/
public void monitorClose() {
ClientUtils.post(url + ENVS_ROOT + instanceId + MONITOR_CLOSE, new JSONObject());
}
/**
* Upload monitoring data to OpenAI servers.
*
* @param trainingDir directory that contains the monitoring data
* @param apiKey personal OpenAI API key
* @param algorithmId an arbitrary string indicating the paricular version of the algorithm (including choices of parameters) you are running.
**/
public void upload(String trainingDir, String apiKey, String algorithmId) {
JSONObject json = new JSONObject().put("training_dir", trainingDir).put("api_key", apiKey).put("algorithm_id",
algorithmId);
uploadPost(json);
}
/**
* Upload monitoring data to OpenAI servers.
*
* @param trainingDir directory that contains the monitoring data
* @param apiKey personal OpenAI API key
*/
public void upload(String trainingDir, String apiKey) {
JSONObject json = new JSONObject().put("training_dir", trainingDir).put("api_key", apiKey);
uploadPost(json);
}
private void uploadPost(JSONObject json) {
try {
ClientUtils.post(url + V1_ROOT + UPLOAD, json);
} catch (RuntimeException e) {
log.error("Impossible to upload: Wrong API key?");
}
}
/**
* Shutdown the server at the same url than this client
*/
public void serverShutdown() {
serverShutdown(url);
}
public ActionSpace getActionSpace(){
return actionSpace;
}
}

90
gym-java-client/src/main/java/org/deeplearning4j/gym/ClientFactory.java
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@ -1,90 +0,0 @@
/*******************************************************************************
* Copyright (c) 2015-2018 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.gym;
import org.deeplearning4j.rl4j.space.ActionSpace;
import org.deeplearning4j.rl4j.space.DiscreteSpace;
import org.deeplearning4j.rl4j.space.GymObservationSpace;
import org.deeplearning4j.rl4j.space.HighLowDiscrete;
import org.json.JSONArray;
import org.json.JSONException;
import org.json.JSONObject;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
/**
* @author rubenfiszel (ruben.fiszel@epfl.ch) on 7/8/16.
*
* ClientFactory contains builder method to create a new {@link Client}
*/
public class ClientFactory {
public static <O, A, AS extends ActionSpace<A>> Client<O, A, AS> build(String url, String envId, boolean render) {
JSONObject body = new JSONObject().put("env_id", envId);
JSONObject reply = ClientUtils.post(url + Client.ENVS_ROOT, body).getObject();
String instanceId;
try {
instanceId = reply.getString("instance_id");
} catch (JSONException e) {
throw new RuntimeException("Environment id not found", e);
}
GymObservationSpace<O> observationSpace = fetchObservationSpace(url, instanceId);
AS actionSpace = fetchActionSpace(url, instanceId);
return new Client(url, envId, instanceId, observationSpace, actionSpace, render);
}
public static <O, A, AS extends ActionSpace<A>> Client<O, A, AS> build(String envId, boolean render) {
return build("http://127.0.0.1:5000", envId, render);
}
public static <AS extends ActionSpace> AS fetchActionSpace(String url, String instanceId) {
JSONObject reply = ClientUtils.get(url + Client.ENVS_ROOT + instanceId + Client.ACTION_SPACE);
JSONObject info = reply.getJSONObject("info");
String infoName = info.getString("name");
switch (infoName) {
case "Discrete":
return (AS) new DiscreteSpace(info.getInt("n"));
case "HighLow":
int numRows = info.getInt("num_rows");
int size = 3 * numRows;
JSONArray matrixJson = info.getJSONArray("matrix");
INDArray matrix = Nd4j.create(numRows, 3);
for (int i = 0; i < size; i++) {
matrix.putScalar(i, matrixJson.getDouble(i));
}
matrix.reshape(numRows, 3);
return (AS) new HighLowDiscrete(matrix);
default:
throw new RuntimeException("Unknown action space " + infoName);
}
}
public static <O> GymObservationSpace<O> fetchObservationSpace(String url, String instanceId) {
JSONObject reply = ClientUtils.get(url + Client.ENVS_ROOT + instanceId + Client.OBSERVATION_SPACE);
JSONObject info = reply.getJSONObject("info");
return new GymObservationSpace<O>(info);
}
}

75
gym-java-client/src/main/java/org/deeplearning4j/gym/ClientUtils.java
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@ -1,75 +0,0 @@
/*******************************************************************************
* Copyright (c) 2015-2018 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.gym;
import com.mashape.unirest.http.HttpResponse;
import com.mashape.unirest.http.JsonNode;
import com.mashape.unirest.http.Unirest;
import com.mashape.unirest.http.exceptions.UnirestException;
import org.json.JSONException;
import org.json.JSONObject;
/**
* @author rubenfiszel (ruben.fiszel@epfl.ch) on 7/8/16.
*
* ClientUtils contain the utility methods to post and get data from the server REST API through the library unirest.
*/
public class ClientUtils {
static public JsonNode post(String url, JSONObject json) {
HttpResponse<JsonNode> jsonResponse = null;
try {
jsonResponse = Unirest.post(url).header("content-type", "application/json").body(json).asJson();
} catch (UnirestException e) {
unirestCrash(e);
}
return jsonResponse.getBody();
}
static public JSONObject get(String url) {
HttpResponse<JsonNode> jsonResponse = null;
try {
jsonResponse = Unirest.get(url).header("content-type", "application/json").asJson();
} catch (UnirestException e) {
unirestCrash(e);
}
checkReply(jsonResponse, url);
return jsonResponse.getBody().getObject();
}
static public void checkReply(HttpResponse<JsonNode> res, String url) {
if (res.getBody() == null)
throw new RuntimeException("Invalid reply at: " + url);
}
static public void unirestCrash(UnirestException e) {
//if couldn't parse json
if (e.getCause().getCause().getCause() instanceof JSONException)
throw new RuntimeException("Couldn't parse json reply.", e);
else
throw new RuntimeException("Connection error", e);
}
}

82
gym-java-client/src/main/java/org/deeplearning4j/rl4j/space/GymObservationSpace.java
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@ -1,82 +0,0 @@
/*******************************************************************************
* Copyright (c) 2015-2018 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.space;
import lombok.Value;
import org.json.JSONArray;
import org.json.JSONObject;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
/**
* @author rubenfiszel (ruben.fiszel@epfl.ch) on 7/8/16.
*
* Contain contextual information about the environment from which Observations are observed and must know how to build an Observation from json.
*
* @param <O> the type of Observation
*/
@Value
public class GymObservationSpace<O> implements ObservationSpace<O> {
String name;
int[] shape;
INDArray low;
INDArray high;
public GymObservationSpace(JSONObject jsonObject) {
name = jsonObject.getString("name");
JSONArray arr = jsonObject.getJSONArray("shape");
int lg = arr.length();
shape = new int[lg];
for (int i = 0; i < lg; i++) {
this.shape[i] = arr.getInt(i);
}
low = Nd4j.create(shape);
high = Nd4j.create(shape);
JSONArray lowJson = jsonObject.getJSONArray("low");
JSONArray highJson = jsonObject.getJSONArray("high");
int size = shape[0];
for (int i = 1; i < shape.length; i++) {
size *= shape[i];
}
for (int i = 0; i < size; i++) {
low.putScalar(i, lowJson.getDouble(i));
high.putScalar(i, highJson.getDouble(i));
}
}
public O getValue(JSONObject o, String key) {
switch (name) {
case "Box":
JSONArray arr = o.getJSONArray(key);
return (O) new Box(arr);
default:
throw new RuntimeException("Invalid environment name: " + name);
}
}
}

140
gym-java-client/src/test/java/org/deeplearning4j/gym/test/ClientTest.java
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@ -1,140 +0,0 @@
/*******************************************************************************
* Copyright (c) 2015-2018 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.gym.test;
import com.mashape.unirest.http.JsonNode;
import org.deeplearning4j.gym.Client;
import org.deeplearning4j.gym.ClientFactory;
import org.deeplearning4j.gym.ClientUtils;
import org.deeplearning4j.gym.StepReply;
import org.deeplearning4j.rl4j.space.ActionSpace;
import org.deeplearning4j.rl4j.space.Box;
import org.deeplearning4j.rl4j.space.DiscreteSpace;
import org.json.JSONObject;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.powermock.core.classloader.annotations.PrepareForTest;
import org.powermock.modules.junit4.PowerMockRunner;
import static org.mockito.Matchers.eq;
import static org.powermock.api.mockito.PowerMockito.mockStatic;
import static org.powermock.api.mockito.PowerMockito.when;
/**
* @author rubenfiszel (ruben.fiszel@epfl.ch) on 7/11/16.
*/
@RunWith(PowerMockRunner.class)
@PrepareForTest(ClientUtils.class)
public class ClientTest {
@Test
public void completeClientTest() {
String url = "http://127.0.0.1:5000";
String env = "Powermock-v0";
String instanceID = "e15739cf";
String testDir = "/tmp/testDir";
boolean render = true;
String renderStr = render ? "True" : "False";
mockStatic(ClientUtils.class);
//post mock
JSONObject buildReq = new JSONObject("{\"env_id\":\"" + env + "\"}");
JsonNode buildRep = new JsonNode("{\"instance_id\":\"" + instanceID + "\"}");
when(ClientUtils.post(eq(url + Client.ENVS_ROOT), JSONObjectMatcher.jsonEq(buildReq))).thenReturn(buildRep);
JSONObject monStartReq = new JSONObject("{\"resume\":false,\"directory\":\"" + testDir + "\",\"force\":true}");
when(ClientUtils.post(eq(url + Client.ENVS_ROOT + instanceID + Client.MONITOR_START),
JSONObjectMatcher.jsonEq(monStartReq))).thenReturn(null);
JSONObject monStopReq = new JSONObject("{}");
when(ClientUtils.post(eq(url + Client.ENVS_ROOT + instanceID + Client.MONITOR_CLOSE),
JSONObjectMatcher.jsonEq(monStopReq))).thenReturn(null);
JSONObject resetReq = new JSONObject("{}");
JsonNode resetRep = new JsonNode(
"{\"observation\":[0.021729452941849317,-0.04764548144956857,-0.024914502756611293,-0.04074903379512588]}");
when(ClientUtils.post(eq(url + Client.ENVS_ROOT + instanceID + Client.RESET),
JSONObjectMatcher.jsonEq(resetReq))).thenReturn(resetRep);
JSONObject stepReq = new JSONObject("{\"action\":0, \"render\":" + renderStr + "}");
JsonNode stepRep = new JsonNode(
"{\"observation\":[0.020776543312857946,-0.24240146656155923,-0.02572948343251381,0.24397017400615437],\"reward\":1,\"done\":false,\"info\":{}}");
when(ClientUtils.post(eq(url + Client.ENVS_ROOT + instanceID + Client.STEP), JSONObjectMatcher.jsonEq(stepReq)))
.thenReturn(stepRep);
JSONObject stepReq2 = new JSONObject("{\"action\":1, \"render\":" + renderStr + "}");
JsonNode stepRep2 = new JsonNode(
"{\"observation\":[0.020776543312857946,-0.24240146656155923,-0.02572948343251381,0.24397017400615437],\"reward\":1,\"done\":false,\"info\":{}}");
when(ClientUtils.post(eq(url + Client.ENVS_ROOT + instanceID + Client.STEP),
JSONObjectMatcher.jsonEq(stepReq2))).thenReturn(stepRep2);
//get mock
JSONObject obsSpace = new JSONObject(
"{\"info\":{\"name\":\"Box\",\"shape\":[4],\"high\":[4.8,3.4028234663852886E38,0.41887902047863906,3.4028234663852886E38],\"low\":[-4.8,-3.4028234663852886E38,-0.41887902047863906,-3.4028234663852886E38]}}");
when(ClientUtils.get(eq(url + Client.ENVS_ROOT + instanceID + Client.OBSERVATION_SPACE))).thenReturn(obsSpace);
JSONObject actionSpace = new JSONObject("{\"info\":{\"name\":\"Discrete\",\"n\":2}}");
when(ClientUtils.get(eq(url + Client.ENVS_ROOT + instanceID + Client.ACTION_SPACE))).thenReturn(actionSpace);
//test
Client<Box, Integer, DiscreteSpace> client = ClientFactory.build(url, env, render);
client.monitorStart(testDir, true, false);
int episodeCount = 1;
int maxSteps = 200;
int reward = 0;
for (int i = 0; i < episodeCount; i++) {
client.reset();
for (int j = 0; j < maxSteps; j++) {
Integer action = ((ActionSpace<Integer>)client.getActionSpace()).randomAction();
StepReply<Box> step = client.step(action);
reward += step.getReward();
//return a isDone true before i == maxSteps
if (j == maxSteps - 5) {
JSONObject stepReqLoc = new JSONObject("{\"action\":0}");
JsonNode stepRepLoc = new JsonNode(
"{\"observation\":[0.020776543312857946,-0.24240146656155923,-0.02572948343251381,0.24397017400615437],\"reward\":1,\"done\":true,\"info\":{}}");
when(ClientUtils.post(eq(url + Client.ENVS_ROOT + instanceID + Client.STEP),
JSONObjectMatcher.jsonEq(stepReqLoc))).thenReturn(stepRepLoc);
}
if (step.isDone()) {
// System.out.println("break");
break;
}
}
}
client.monitorClose();
client.upload(testDir, "YOUR_OPENAI_GYM_API_KEY");
}
}

46
gym-java-client/src/test/java/org/deeplearning4j/gym/test/JSONObjectMatcher.java
View File

@ -1,46 +0,0 @@
/*******************************************************************************
* Copyright (c) 2015-2018 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.gym.test;
import org.json.JSONObject;
import org.mockito.ArgumentMatcher;
import static org.mockito.Matchers.argThat;
/**
* @author rubenfiszel (ruben.fiszel@epfl.ch) on 7/11/16.
*/
public class JSONObjectMatcher implements ArgumentMatcher<JSONObject> {
private final JSONObject expected;
public JSONObjectMatcher(JSONObject expected) {
this.expected = expected;
}
public static JSONObject jsonEq(JSONObject expected) {
return argThat(new JSONObjectMatcher(expected));
}
@Override
public boolean matches(JSONObject argument) {
if (expected == null)
return argument == null;
return expected.toString().equals(argument.toString()); }
}

22
pom.xml
View File

@ -132,7 +132,6 @@
<module>deeplearning4j</module>
<module>arbiter</module>
<module>nd4s</module>
<module>gym-java-client</module>
<module>rl4j</module>
<module>scalnet</module>
<module>jumpy</module>
@ -288,22 +287,23 @@
<javacpp.platform.extension/> <!-- -Djavacpp.platform.extension=-avx512 -->
<javacpp.platform.properties>${javacpp.platform}</javacpp.platform.properties>
<javacpp.version>1.5.2</javacpp.version>
<javacpp-presets.version>1.5.2</javacpp-presets.version>
<javacv.version>1.5.2</javacv.version>
<javacpp.version>1.5.3-SNAPSHOT</javacpp.version>
<javacpp-presets.version>1.5.3-SNAPSHOT</javacpp-presets.version>
<javacv.version>1.5.3-SNAPSHOT</javacv.version>
<python.version>3.7.5</python.version>
<python.version>3.7.6</python.version>
<cpython-platform.version>${python.version}-${javacpp-presets.version}</cpython-platform.version>
<numpy.version>1.17.3</numpy.version>
<numpy.version>1.18.0</numpy.version>
<numpy.javacpp.version>${numpy.version}-${javacpp-presets.version}</numpy.javacpp.version>
<openblas.version>0.3.7</openblas.version>
<mkl.version>2019.5</mkl.version>
<opencv.version>4.1.2</opencv.version>
<ffmpeg.version>4.2.1</ffmpeg.version>
<leptonica.version>1.78.0</leptonica.version>
<hdf5.version>1.10.5</hdf5.version>
<ale.version>0.6.0</ale.version>
<opencv.version>4.2.0</opencv.version>
<ffmpeg.version>4.2.2</ffmpeg.version>
<leptonica.version>1.79.0</leptonica.version>
<hdf5.version>1.10.6</hdf5.version>
<ale.version>0.6.1</ale.version>
<gym.version>0.15.4</gym.version>
<tensorflow.version>1.15.0</tensorflow.version>
<tensorflow.javacpp.version>${tensorflow.version}-${javacpp-presets.version}</tensorflow.javacpp.version>

10
rl4j/README.md
View File

@ -32,10 +32,6 @@ Comments are welcome on our gitter channel:
# Quickstart
** INSTALL rl4j-api before installing all (see below)!**
* mvn install -pl rl4j-api
* [if you want rl4j-gym too] Download and mvn install: [gym-java-client](https://github.com/eclipse/deeplearning4j/tree/master/gym-java-client)
* mvn install
# Visualisation
@ -44,9 +40,7 @@ Comments are welcome on our gitter channel:
# Quicktry cartpole:
* Install [gym-http-api](https://github.com/openai/gym-http-api).
* launch http api server.
* run with this [main](https://github.com/rubenfiszel/rl4j-examples/blob/master/src/main/java/org/deeplearning4j/rl4j/Cartpole.java)
* run with this [main](https://github.com/eclipse/deeplearning4j-examples/blob/master/rl4j-examples/src/main/java/org/deeplearning4j/examples/rl4j/Cartpole.java)
# Doom
@ -83,4 +77,4 @@ Doom is not ready yet but you can make it work if you feel adventurous with some
* Continuous control
* Policy Gradient
* Update gym-java-client when gym-http-api gets compatible with pixels environments to play with Pong, Doom, etc ..
* Update rl4j-gym to make it compatible with pixels environments to play with Pong, Doom, etc ..

50
rl4j/pom.xml
View File

@ -97,6 +97,30 @@
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-enforcer-plugin</artifactId>
<version>${maven-enforcer-plugin.version}</version>
<executions>
<execution>
<phase>test</phase>
<id>enforce-choice-of-nd4j-test-backend</id>
<goals>
<goal>enforce</goal>
</goals>
<configuration>
<skip>${skipBackendChoice}</skip>
<rules>
<requireActiveProfile>
<profiles>test-nd4j-native,test-nd4j-cuda-10.2</profiles>
<all>false</all>
</requireActiveProfile>
</rules>
<fail>true</fail>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<artifactId>maven-source-plugin</artifactId>
<version>${maven-source-plugin.version}</version>
@ -265,6 +289,32 @@
</pluginManagement>
</build>
<profiles>
<profile>
<id>test-nd4j-native</id>
<dependencies>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-native</artifactId>
<version>${nd4j.version}</version>
<scope>test</scope>
</dependency>
</dependencies>
</profile>
<profile>
<id>test-nd4j-cuda-10.2</id>
<dependencies>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-cuda-10.2</artifactId>
<version>${nd4j.version}</version>
<scope>test</scope>
</dependency>
</dependencies>
</profile>
</profiles>
<reporting>
<plugins>
<plugin>

9
rl4j/rl4j-ale/pom.xml
View File

@ -44,4 +44,13 @@
<version>${ale.version}-${javacpp-presets.version}</version>
</dependency>
</dependencies>
<profiles>
<profile>
<id>test-nd4j-native</id>
</profile>
<profile>
<id>test-nd4j-cuda-10.2</id>
</profile>
</profiles>
</project>

14
rl4j/rl4j-api/pom.xml
View File

@ -33,15 +33,19 @@
</properties>
<dependencies>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>gym-java-client</artifactId>
<version>${dl4j.version}</version>
</dependency>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-api</artifactId>
<version>${nd4j.version}</version>
</dependency>
</dependencies>
<profiles>
<profile>
<id>test-nd4j-native</id>
</profile>
<profile>
<id>test-nd4j-cuda-10.2</id>
</profile>
</profiles>
</project>

3
gym-java-client/src/main/java/org/deeplearning4j/gym/StepReply.java → rl4j/rl4j-api/src/main/java/org/deeplearning4j/gym/StepReply.java
View File

@ -17,7 +17,6 @@
package org.deeplearning4j.gym;
import lombok.Value;
import org.json.JSONObject;
/**
* @param <T> type of observation
@ -31,6 +30,6 @@ public class StepReply<T> {
T observation;
double reward;
boolean done;
JSONObject info;
Object info;
}

0
gym-java-client/src/main/java/org/deeplearning4j/rl4j/space/ActionSpace.java → rl4j/rl4j-api/src/main/java/org/deeplearning4j/rl4j/space/ActionSpace.java
View File

0
gym-java-client/src/main/java/org/deeplearning4j/rl4j/space/ArrayObservationSpace.java → rl4j/rl4j-api/src/main/java/org/deeplearning4j/rl4j/space/ArrayObservationSpace.java
View File

12
gym-java-client/src/main/java/org/deeplearning4j/rl4j/space/Box.java → rl4j/rl4j-api/src/main/java/org/deeplearning4j/rl4j/space/Box.java
View File

@ -16,8 +16,6 @@
package org.deeplearning4j.rl4j.space;
import org.json.JSONArray;
/**
* @author rubenfiszel (ruben.fiszel@epfl.ch) on 7/8/16.
*
@ -29,14 +27,8 @@ public class Box implements Encodable {
private final double[] array;
public Box(JSONArray arr) {
int lg = arr.length();
this.array = new double[lg];
for (int i = 0; i < lg; i++) {
this.array[i] = arr.getDouble(i);
}
public Box(double[] arr) {
this.array = arr;
}
public double[] toArray() {

0
gym-java-client/src/main/java/org/deeplearning4j/rl4j/space/DiscreteSpace.java → rl4j/rl4j-api/src/main/java/org/deeplearning4j/rl4j/space/DiscreteSpace.java
View File

0
gym-java-client/src/main/java/org/deeplearning4j/rl4j/space/Encodable.java → rl4j/rl4j-api/src/main/java/org/deeplearning4j/rl4j/space/Encodable.java
View File

12
gym-java-client/src/main/java/org/deeplearning4j/rl4j/space/HighLowDiscrete.java → rl4j/rl4j-api/src/main/java/org/deeplearning4j/rl4j/space/HighLowDiscrete.java
View File

@ -17,10 +17,8 @@
package org.deeplearning4j.rl4j.space;
import lombok.Value;
import org.json.JSONArray;
import org.nd4j.linalg.api.ndarray.INDArray;
/**
* @author rubenfiszel (ruben.fiszel@epfl.ch) on 7/26/16.
*/
@ -37,12 +35,8 @@ public class HighLowDiscrete extends DiscreteSpace {
@Override
public Object encode(Integer a) {
JSONArray jsonArray = new JSONArray();
for (int i = 0; i < size; i++) {
jsonArray.put(matrix.getDouble(i, 0));
}
jsonArray.put(a - 1, matrix.getDouble(a - 1, 1));
return jsonArray;
INDArray m = matrix.dup();
m.put(a - 1, 0, matrix.getDouble(a - 1, 1));
return m;
}
}

0
gym-java-client/src/main/java/org/deeplearning4j/rl4j/space/ObservationSpace.java → rl4j/rl4j-api/src/main/java/org/deeplearning4j/rl4j/space/ObservationSpace.java
View File

26
rl4j/rl4j-core/pom.xml
View File

@ -44,11 +44,6 @@
<groupId>ch.qos.logback</groupId>
<artifactId>logback-classic</artifactId>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>gym-java-client</artifactId>
<version>${dl4j.version}</version>
</dependency>
<dependency>
<groupId>org.bytedeco</groupId>
@ -111,27 +106,10 @@
<profiles>
<profile>
<id>nd4j-tests-cpu</id>
<dependencies>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-native</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
</dependencies>
<id>test-nd4j-native</id>
</profile>
<profile>
<id>nd4j-tests-cuda</id>
<dependencies>
<dependency>
<groupId>org.nd4j</groupId>
<artifactId>nd4j-cuda-10.2</artifactId>
<version>${project.version}</version>
<scope>test</scope>
</dependency>
</dependencies>
<id>test-nd4j-cuda-10.2</id>
</profile>
</profiles>
</project>

3
rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/mdp/toy/HardDeteministicToy.java
View File

@ -23,7 +23,6 @@ import org.deeplearning4j.rl4j.network.dqn.IDQN;
import org.deeplearning4j.rl4j.space.ArrayObservationSpace;
import org.deeplearning4j.rl4j.space.DiscreteSpace;
import org.deeplearning4j.rl4j.space.ObservationSpace;
import org.json.JSONObject;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
@ -104,7 +103,7 @@ public class HardDeteministicToy implements MDP<HardToyState, Integer, DiscreteS
if (a == maxIndex(hardToyState.getValues()))
reward += 1;
hardToyState = states[hardToyState.getStep() + 1];
return new StepReply(hardToyState, reward, isDone(), new JSONObject("{}"));
return new StepReply(hardToyState, reward, isDone(), null);
}
public HardDeteministicToy newInstance() {

3
rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/mdp/toy/SimpleToy.java
View File

@ -26,7 +26,6 @@ import org.deeplearning4j.rl4j.network.dqn.IDQN;
import org.deeplearning4j.rl4j.space.ArrayObservationSpace;
import org.deeplearning4j.rl4j.space.DiscreteSpace;
import org.deeplearning4j.rl4j.space.ObservationSpace;
import org.json.JSONObject;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
@ -80,7 +79,7 @@ public class SimpleToy implements MDP<SimpleToyState, Integer, DiscreteSpace> {
public StepReply<SimpleToyState> step(Integer a) {
double reward = (simpleToyState.getStep() % 2 == 0) ? 1 - a : a;
simpleToyState = new SimpleToyState(simpleToyState.getI() + 1, simpleToyState.getStep() + 1);
return new StepReply<>(simpleToyState, reward, isDone(), new JSONObject("{}"));
return new StepReply<>(simpleToyState, reward, isDone(), null);
}
public SimpleToy newInstance() {

49
rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/util/DataManager.java
View File

@ -26,6 +26,7 @@ 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.network.ac.IActorCritic;
import org.deeplearning4j.rl4j.network.dqn.DQN;
import org.deeplearning4j.rl4j.network.dqn.IDQN;
import org.deeplearning4j.util.ModelSerializer;
@ -88,19 +89,38 @@ public class DataManager implements IDataManager {
String json = new ObjectMapper().writeValueAsString(learning.getConfiguration());
writeEntry(new ByteArrayInputStream(json.getBytes()), zipfile);
ZipEntry dqn = new ZipEntry("dqn.bin");
zipfile.putNextEntry(dqn);
try {
ZipEntry dqn = new ZipEntry("dqn.bin");
zipfile.putNextEntry(dqn);
ByteArrayOutputStream bos = new ByteArrayOutputStream();
if(learning instanceof NeuralNetFetchable) {
((NeuralNetFetchable)learning).getNeuralNet().save(bos);
ByteArrayOutputStream bos = new ByteArrayOutputStream();
if(learning instanceof NeuralNetFetchable) {
((NeuralNetFetchable)learning).getNeuralNet().save(bos);
}
bos.flush();
bos.close();
InputStream inputStream = new ByteArrayInputStream(bos.toByteArray());
writeEntry(inputStream, zipfile);
} catch (UnsupportedOperationException e) {
ByteArrayOutputStream bos = new ByteArrayOutputStream();
ByteArrayOutputStream bos2 = new ByteArrayOutputStream();
((IActorCritic)((NeuralNetFetchable)learning).getNeuralNet()).save(bos, bos2);
bos.flush();
bos.close();
InputStream inputStream = new ByteArrayInputStream(bos.toByteArray());
ZipEntry value = new ZipEntry("value.bin");
zipfile.putNextEntry(value);
writeEntry(inputStream, zipfile);
bos2.flush();
bos2.close();
InputStream inputStream2 = new ByteArrayInputStream(bos2.toByteArray());
ZipEntry policy = new ZipEntry("policy.bin");
zipfile.putNextEntry(policy);
writeEntry(inputStream2, zipfile);
}
bos.flush();
bos.close();
InputStream inputStream = new ByteArrayInputStream(bos.toByteArray());
writeEntry(inputStream, zipfile);
if (learning.getHistoryProcessor() != null) {
ZipEntry hpconf = new ZipEntry("hpconf.bin");
@ -268,7 +288,12 @@ public class DataManager implements IDataManager {
save(getModelDir() + "/" + learning.getStepCounter() + ".training", learning);
if(learning instanceof NeuralNetFetchable) {
((NeuralNetFetchable)learning).getNeuralNet().save(getModelDir() + "/" + learning.getStepCounter() + ".model");
try {
((NeuralNetFetchable)learning).getNeuralNet().save(getModelDir() + "/" + learning.getStepCounter() + ".model");
} catch (UnsupportedOperationException e) {
String path = getModelDir() + "/" + learning.getStepCounter();
((IActorCritic)((NeuralNetFetchable)learning).getNeuralNet()).save(path + "_value.model", path + "_policy.model");
}
}
}

5
rl4j/rl4j-core/src/main/java/org/deeplearning4j/rl4j/util/LegacyMDPWrapper.java
View File

@ -47,7 +47,10 @@ public class LegacyMDPWrapper<O, A, AS extends ActionSpace<A>> implements MDP<Ob
return historyProcessor;
}
return learning.getHistoryProcessor();
if (learning != null) {
return learning.getHistoryProcessor();
}
return null;
}
public void setHistoryProcessor(IHistoryProcessor historyProcessor) {

9
rl4j/rl4j-doom/pom.xml
View File

@ -39,4 +39,13 @@
<version>${project.version}</version>
</dependency>
</dependencies>
<profiles>
<profile>
<id>test-nd4j-native</id>
</profile>
<profile>
<id>test-nd4j-cuda-10.2</id>
</profile>
</profiles>
</project>

15
rl4j/rl4j-gym/pom.xml
View File

@ -39,9 +39,18 @@
<version>${project.version}</version>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>gym-java-client</artifactId>
<version>${project.version}</version>
<groupId>org.bytedeco</groupId>
<artifactId>gym-platform</artifactId>
<version>${gym.version}-${javacpp-presets.version}</version>
</dependency>
</dependencies>
<profiles>
<profile>
<id>test-nd4j-native</id>
</profile>
<profile>
<id>test-nd4j-cuda-10.2</id>
</profile>
</profiles>
</project>

169
rl4j/rl4j-gym/src/main/java/org/deeplearning4j/rl4j/mdp/gym/GymEnv.java
View File

@ -1,5 +1,6 @@
/*******************************************************************************
* 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
@ -16,39 +17,111 @@
package org.deeplearning4j.rl4j.mdp.gym;
import org.deeplearning4j.gym.Client;
import org.deeplearning4j.gym.ClientFactory;
import java.io.IOException;
import lombok.Getter;
import lombok.Setter;
import lombok.Value;
import lombok.extern.slf4j.Slf4j;
import org.bytedeco.javacpp.DoublePointer;
import org.bytedeco.javacpp.Pointer;
import org.bytedeco.javacpp.SizeTPointer;
import org.deeplearning4j.gym.StepReply;
import org.deeplearning4j.rl4j.mdp.MDP;
import org.deeplearning4j.rl4j.space.ArrayObservationSpace;
import org.deeplearning4j.rl4j.space.ActionSpace;
import org.deeplearning4j.rl4j.space.Box;
import org.deeplearning4j.rl4j.space.DiscreteSpace;
import org.deeplearning4j.rl4j.space.Encodable;
import org.deeplearning4j.rl4j.space.HighLowDiscrete;
import org.deeplearning4j.rl4j.space.ObservationSpace;
import org.bytedeco.cpython.*;
import org.bytedeco.numpy.*;
import static org.bytedeco.cpython.global.python.*;
import static org.bytedeco.numpy.global.numpy.*;
/**
* An MDP for OpenAI Gym: https://gym.openai.com/
*
* @author rubenfiszel (ruben.fiszel@epfl.ch) 7/12/16.
*
* Wrapper over the client of gym-java-client
*
* @author saudet
*/
@Slf4j
public class GymEnv<O, A, AS extends ActionSpace<A>> implements MDP<O, A, AS> {
final public static String GYM_MONITOR_DIR = "/tmp/gym-dqn";
public static final String GYM_MONITOR_DIR = "/tmp/gym-dqn";
final private Client<O, A, AS> client;
private static void checkPythonError() {
if (PyErr_Occurred() != null) {
PyErr_Print();
throw new RuntimeException("Python error occurred");
}
}
private static Pointer program;
private static PyObject globals;
static {
try {
Py_SetPath(org.bytedeco.gym.presets.gym.cachePackages());
program = Py_DecodeLocale(GymEnv.class.getSimpleName(), null);
Py_SetProgramName(program);
Py_Initialize();
PyEval_InitThreads();
PySys_SetArgvEx(1, program, 0);
if (_import_array() < 0) {
PyErr_Print();
throw new RuntimeException("numpy.core.multiarray failed to import");
}
globals = PyModule_GetDict(PyImport_AddModule("__main__"));
PyEval_SaveThread(); // just to release the GIL
} catch (IOException e) {
PyMem_RawFree(program);
throw new RuntimeException(e);
}
}
private PyObject locals;
final protected DiscreteSpace actionSpace;
final protected ObservationSpace<O> observationSpace;
@Getter
final private String envId;
@Getter
final private boolean render;
@Getter
final private boolean monitor;
private ActionTransformer actionTransformer = null;
private boolean done = false;
public GymEnv(String envId, boolean render, boolean monitor) {
this.client = ClientFactory.build(envId, render);
this.envId = envId;
this.render = render;
this.monitor = monitor;
if (monitor)
client.monitorStart(GYM_MONITOR_DIR, true, false);
int gstate = PyGILState_Ensure();
try {
locals = PyDict_New();
Py_DecRef(PyRun_StringFlags("import gym; env = gym.make('" + envId + "')", Py_single_input, globals, locals, null));
checkPythonError();
if (monitor) {
Py_DecRef(PyRun_StringFlags("env = gym.wrappers.Monitor(env, '" + GYM_MONITOR_DIR + "')", Py_single_input, globals, locals, null));
checkPythonError();
}
PyObject shapeTuple = PyRun_StringFlags("env.observation_space.shape", Py_eval_input, globals, locals, null);
int[] shape = new int[(int)PyTuple_Size(shapeTuple)];
for (int i = 0; i < shape.length; i++) {
shape[i] = (int)PyLong_AsLong(PyTuple_GetItem(shapeTuple, i));
}
observationSpace = (ObservationSpace<O>) new ArrayObservationSpace<Box>(shape);
Py_DecRef(shapeTuple);
PyObject n = PyRun_StringFlags("env.action_space.n", Py_eval_input, globals, locals, null);
actionSpace = new DiscreteSpace((int)PyLong_AsLong(n));
Py_DecRef(n);
checkPythonError();
} finally {
PyGILState_Release(gstate);
}
}
public GymEnv(String envId, boolean render, boolean monitor, int[] actions) {
@ -56,43 +129,87 @@ public class GymEnv<O, A, AS extends ActionSpace<A>> implements MDP<O, A, AS> {
actionTransformer = new ActionTransformer((HighLowDiscrete) getActionSpace(), actions);
}
@Override
public ObservationSpace<O> getObservationSpace() {
return client.getObservationSpace();
return observationSpace;
}
@Override
public AS getActionSpace() {
if (actionTransformer == null)
return (AS) client.getActionSpace();
return (AS) actionSpace;
else
return (AS) actionTransformer;
}
@Override
public StepReply<O> step(A action) {
StepReply<O> stepRep = client.step(action);
done = stepRep.isDone();
return stepRep;
int gstate = PyGILState_Ensure();
try {
if (render) {
Py_DecRef(PyRun_StringFlags("env.render()", Py_single_input, globals, locals, null));
checkPythonError();
}
Py_DecRef(PyRun_StringFlags("state, reward, done, info = env.step(" + (Integer)action +")", Py_single_input, globals, locals, null));
checkPythonError();
PyArrayObject state = new PyArrayObject(PyDict_GetItemString(locals, "state"));
DoublePointer stateData = new DoublePointer(PyArray_BYTES(state)).capacity(PyArray_Size(state));
SizeTPointer stateDims = PyArray_DIMS(state).capacity(PyArray_NDIM(state));
double reward = PyFloat_AsDouble(PyDict_GetItemString(locals, "reward"));
done = PyLong_AsLong(PyDict_GetItemString(locals, "done")) != 0;
checkPythonError();
double[] data = new double[(int)stateData.capacity()];
stateData.get(data);
return new StepReply(new Box(data), reward, done, null);
} finally {
PyGILState_Release(gstate);
}
}
@Override
public boolean isDone() {
return done;
}
@Override
public O reset() {
done = false;
return client.reset();
}
public void upload(String apiKey) {
client.upload(GYM_MONITOR_DIR, apiKey);
int gstate = PyGILState_Ensure();
try {
Py_DecRef(PyRun_StringFlags("state = env.reset()", Py_single_input, globals, locals, null));
checkPythonError();
PyArrayObject state = new PyArrayObject(PyDict_GetItemString(locals, "state"));
DoublePointer stateData = new DoublePointer(PyArray_BYTES(state)).capacity(PyArray_Size(state));
SizeTPointer stateDims = PyArray_DIMS(state).capacity(PyArray_NDIM(state));
checkPythonError();
done = false;
double[] data = new double[(int)stateData.capacity()];
stateData.get(data);
return (O) new Box(data);
} finally {
PyGILState_Release(gstate);
}
}
@Override
public void close() {
if (monitor)
client.monitorClose();
int gstate = PyGILState_Ensure();
try {
Py_DecRef(PyRun_StringFlags("env.close()", Py_single_input, globals, locals, null));
checkPythonError();
Py_DecRef(locals);
} finally {
PyGILState_Release(gstate);
}
}
@Override
public GymEnv<O, A, AS> newInstance() {
return new GymEnv<O, A, AS>(envId, render, monitor);
}

48
rl4j/rl4j-gym/src/test/java/org/deeplearning4j/rl4j/mdp/gym/GymEnvTest.java Normal file
View File

@ -0,0 +1,48 @@
/*******************************************************************************
* 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.mdp.gym;
import org.deeplearning4j.gym.StepReply;
import org.deeplearning4j.rl4j.space.ArrayObservationSpace;
import org.deeplearning4j.rl4j.space.Box;
import org.deeplearning4j.rl4j.space.DiscreteSpace;
import org.junit.Test;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertNotEquals;
/**
*
* @author saudet
*/
public class GymEnvTest {
@Test
public void testCartpole() {
GymEnv mdp = new GymEnv("CartPole-v0", false, false);
assertArrayEquals(new int[] {4}, ((ArrayObservationSpace)mdp.getObservationSpace()).getShape());
assertEquals(2, ((DiscreteSpace)mdp.getActionSpace()).getSize());
assertEquals(false, mdp.isDone());
Box o = (Box)mdp.reset();
StepReply r = mdp.step(0);
assertEquals(4, o.toArray().length);
assertEquals(4, ((Box)r.getObservation()).toArray().length);
assertNotEquals(null, mdp.newInstance());
mdp.close();
}
}

14
rl4j/rl4j-malmo/pom.xml
View File

@ -33,6 +33,11 @@
</properties>
<dependencies>
<dependency>
<groupId>org.json</groupId>
<artifactId>json</artifactId>
<version>20190722</version>
</dependency>
<dependency>
<groupId>org.deeplearning4j</groupId>
<artifactId>rl4j-api</artifactId>
@ -44,4 +49,13 @@
<version>0.30.0</version>
</dependency>
</dependencies>
<profiles>
<profile>
<id>test-nd4j-native</id>
</profile>
<profile>
<id>test-nd4j-cuda-10.2</id>
</profile>
</profiles>
</project>