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JAX (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
| Date | Stars |
|---|---|
| 2026-07-31 | 757 |
| 2026-08-01 | 757 |
| 2026-08-02 | 757 |
| 2026-08-06 | 757 |
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[](https://zenodo.org/badge/latestdoi/330059679)
# JAX (Flax) RL
This repository contains JAX (Flax) implementations of Reinforcement Learning algorithms:
* [Soft Actor Critic with learnable temperature](https://arxiv.org/abs/1812.05905)
* [Advantage Weighted Actor Critic](https://arxiv.org/abs/2006.09359)
* [Image Augmentation Is All You Need](https://arxiv.org/abs/2004.13649)(only [K=1, M=1])
* [Deep Deterministic Policy Gradient](https://arxiv.org/abs/1509.02971) with [Clipped Double Q-Learning](https://arxiv.org/abs/1802.09477)
* [Randomized Ensembled Double Q-Learning: Learning Fast Without a Model](https://arxiv.org/abs/2101.05982)
* 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](https://github.com/rail-berkeley/softlearning/), [AWAC](https://github.com/vitchyr/rlkit/tree/master/examples/awac), [DrQ](https://github.com/denisyarats/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}
}
```
You can find an updated version of this repository [here](https://github.com/ikostrikov/jaxrl2).
# Changelog
## January 10th, 2022
- Added an implementation of [Randomized Ensembled Double Q-Learning: Learning Fast Without a Model](https://arxiv.org/abs/2101.05982)
## July 20th, 2021
- Added an implementation of [Deep Deterministic Policy Gradient](https://arxiv.org/abs/1509.02971) with [Clipped Double Q-Learning](https://arxiv.org/abs/1802.09477)
## May 19th, 2021
- Added an implementation of [Soft Actor Critic v1](https://arxiv.org/abs/1801.01290)
## April 29th, 2021
- Added an implementation of data augmentation from [Image Augmentation Is All You Need](https://arxiv.org/abs/2004.13649)
# Installation
Prerequisites:
* Python 3.8-3.9 (not yet 3.10)
* [Poetry](https://python-poetry.org)
* patchelf
Suggested build environment:
```bash
# general build dependencies
sudo apt-get update; sudo apt-get install make build-essential libssl-dev zlib1g-dev \
libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \
libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev
# mujoco dependencies
apt-get -y install wget unzip software-properties-common \
libgl1-mesa-dev \
libgl1-mesa-glx \
libglew-dev \
libosmesa6-dev patchelf
# mujoco installation
curl -OL https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz
mkdir ~/.mujoco
tar -zxf mujoco210-linux-x86_64.tar.gz -C ~/.mujoco
rm mujoco210-linux-x86_64.tar.gz
```
To install, run
```bash
poetry install
# For GPU support run
pip install "jax[cuda]==0.3.10" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
```
For further instructions on running this code on GPU, please follow instructions from [the official repository](https://github.com/google/jax).
For MuJoCo inslattion, you may need to add the following lines in the `.bashrc`:
```bash
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/costa/.mujoco/mujoco210/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia
```
# Development
If you want to modify the code, install following the instructions above.
# [Examples](examples/)
# Troubleshooting
If you experience out-of-memory errors, especially with enabled video saving, please consider reading [docs](https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html#gpu-memory-allocation) on JAX GPU memory allocation. Also, you can try running with the following environment variable:
```bash
XLA_PYTHON_CLIENT_MEM_FRACTION=0.80 python ...
```
If you run your code on a remote machine and want to save videos for DeepExcerpt of 5,350 characters
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81
Ilya Kostrikov
34
Costa Huang · @periodic · United States
15
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:1e7d1372b1b83cb3, topic:reinforcement-learning, topic:deep-reinforcement-learning, desc:reinforcement learning
matched fp:1e7d1372b1b83cb3, topic:deep-learning, topic:jax