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 ..