JAX (Flax) RL

This repository contains JAX (Flax) implementations of Reinforcement Learning algorithms:

Soft Actor Critic with learnable temperature
Advantage Weighted Actor Critic
Image Augmentation Is All You Need(only [K=1, M=1])
Deep Deterministic Policy Gradient with Clipped Double Q-Learning
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model
Behavioral Cloning

The goal of this repository is to provide simple and clean implementations to build research on top of. Please do not use this repository for baseline results and use the original implementations instead (SAC, AWAC, DrQ).

If you use JAXRL in your work, please cite this repository in publications:

@misc{jaxrl,
  author = {Kostrikov, Ilya},
  doi = {10.5281/zenodo.5535154},
  month = {10},
  title = {{JAXRL: Implementations of Reinforcement Learning algorithms in JAX}},
  url = {https://github.com/ikostrikov/jaxrl},
  year = {2021}
}

Changelog

January 10th, 2022

Added an implementation of Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

July 20th, 2021

Added an implementation of Deep Deterministic Policy Gradient with Clipped Double Q-Learning

May 19th, 2021

Added an implementation of Soft Actor Critic v1

April 29th, 2021

Added an implementation of data augmentation from Image Augmentation Is All You Need

Installation

conda install patchelf
pip install dm_control
pip install --upgrade git+https://github.com/ikostrikov/jaxrl
# For GPU support run
pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_releases.html

If you want to run this code on GPU, please follow instructions from the official repository.

Please follow the instructions to build mujoco-py with fast headless GPU rendering.

Development

If you want to modify the code, install following the instructions below.

conda install patchelf
pip install --upgrade -e .

Examples

Troubleshooting

If you experience out-of-memory errors, especially with enabled video saving, please consider reading docs on JAX GPU memory allocation. Also, you can try running with the following environment variable:

XLA_PYTHON_CLIENT_MEM_FRACTION=0.80 python ...

If you run your code on a remote machine and want to save videos for DeepMind Control Suite, please use EGL for rendering:

MUJOCO_GL=egl python train.py --env_name=cheetah-run --save_dir=./tmp/ --save_video

Tensorboard

Launch tensorboard to see training and evaluation logs

tensorboard --logdir=./tmp/

Results

Continous control from states

Continous control from pixels

Docker

Build

Copy your MuJoCo key to ./vendor

cd remote
docker build -t ikostrikov/jaxrl . -f Dockerfile

Test

 sudo docker run -v <examples-dir>:/jaxrl/ ikostrikov/jaxrl:latest python /jaxrl/train.py --env_name=HalfCheetah-v2 --save_dir=/jaxrl/tmp/

# On GPU
 sudo docker run --rm --gpus all -v <examples-dir>:/jaxrl/ --gpus=all ikostrikov/jaxrl:latest python /jaxrl/train.py --env_name=HalfCheetah-v2 --save_dir=/jaxrl/tmp/

Contributing

When contributing to this repository, please first discuss the change you wish to make via issue. If you are not familiar with pull requests, please read this documentation.

Acknowledgements

Thanks to @evgenii-nikishin for helping with JAX. And @dibyaghosh for helping with vmapped ensembles.

vwxyzjn / jaxrl