1a35ebec2e
* Starting to switch configs of RL algorithms to use more fluent builder patterns. Many parameter choices in different algorithms default to SOTA and only be changed in specific cases Signed-off-by: Bam4d <chris.bam4d@gmail.com> * remove personal gpu-build file Signed-off-by: Bam4d <chrisbam4d@gmail.com> * refactored out configurations so they are heirarchical and re-usable, this is a step towards having a plug-and-play framework for different algorithms * backwardly compatible configurations * adding documentation to new configuration classes Signed-off-by: Bam4d <chrisbam4d@gmail.com> * private access modifiers are better suited here Signed-off-by: Bam4d <chrisbam4d@gmail.com> * RL4j does not compile without java 8 due to previous updates fixing null pointers when listener arrays are empty Signed-off-by: Bam4d <chrisbam4d@gmail.com> * fixing copyright headers Signed-off-by: Bam4d <chrisbam4d@gmail.com> * uncomment logging line Signed-off-by: Bam4d <chrisbam4d@gmail.com> * fixing default value for learningUpdateFrequency fixing test failure due to #352 Signed-off-by: Bam4d <chrisbam4d@gmail.com> Co-authored-by: Bam4d <chris.bam4d@gmail.com> |
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| rl4j-gym | ||
| rl4j-malmo | ||
| LICENSE.txt | ||
| README.md | ||
| cartpole.gif | ||
| doom.gif | ||
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README.md
RL4J: Reinforcement Learning for Java
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:
Disclaimer
This is a tech preview and distributed as is. Comments are welcome on our gitter channel: gitter
Quickstart
- mvn install
Visualisation
Quicktry cartpole:
- 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
Proposed contribution area:
- Continuous control
- Policy Gradient
- Update rl4j-gym to make it compatible with pixels environments to play with Pong, Doom, etc ..