RL4J

RL4J is a reinforcement learning framework integrated with deeplearning4j and released under an Apache 2.0 open-source license. By contributing code to this repository, you agree to make your contribution available under an Apache 2.0 license.

• DQN (Deep Q Learning with double DQN)
• Async RL (A3C, Async NStepQlearning)

Both for Low-Dimensional (array of info) and high-dimensional (pixels) input.

Here is a useful blog post I wrote to introduce you to reinforcement learning, DQN and Async RL:

Blog post

Examples

Cartpole example

Disclaimer

This is a tech preview and distributed as is. Comments are welcome on our gitter channel: gitter

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
• mvn install

Visualisation

webapp-rl4j

Quicktry cartpole:

• Install gym-http-api.
• launch http api server.
• run with this main

Doom

Doom is not ready yet but you can make it work if you feel adventurous with some additional steps:

• You will need vizdoom, compile the native lib and move it into the root of your project in a folder
• export MAVEN_OPTS=-Djava.library.path=THEFOLDEROFTHELIB
• mvn compile exec:java -Dexec.mainClass="YOURMAINCLASS"

Malmo (Minecraft)

• Download and unzip Malmo from here
• export MALMO_HOME=YOURMALMO_FOLDER
• export MALMO_XSD_PATH=$MALMO_HOME/Schemas
• launch malmo per instructions
• run with this main

WIP

• Documentation
• Serialization/Deserialization (load save)
• Compression of pixels in order to store 1M state in a reasonnable amount of memory
• Async learning: A3C and nstep learning (requires some missing features from dl4j (calc and apply gradients)).

Author

Ruben Fiszel

Proposed contribution area:

• Continuous control
• Policy Gradient
• Update gym-java-client when gym-http-api gets compatible with pixels environments to play with Pong, Doom, etc ..