JoyRL is a parallel reinforcement learning library based on PyTorch and Ray. Unlike existing RL libraries, JoyRL is helping users to release the burden of implementing algorithms with tough details, unfriendly APIs, and etc. JoyRL is designed for users to train and test RL algorithms with only hyperparameters configuration, which is mush easier for beginners to learn and use. Also, JoyRL supports plenties of state-of-art RL algorithms including RLHF(core of ChatGPT)(See algorithms below). JoyRL provides a modularized framework for users as well to customize their own algorithms and environments.

⚠️ Note that donot install JoyRL through any mirror image!!!

# you need to install Anaconda first
conda create -n joyrl python=3.8
conda activate joyrl
pip install -U joyrl

Torch install:

Pip install is recommended, but if you encounter network error, you can try conda install or pip install with mirrors.

# pip CPU only
pip install torch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0
# if network error, then GPU with mirror image
pip install torch==1.10.0+cu113 torchvision==0.11.0+cu113 torchaudio==0.10.0 --extra-index-url https://download.pytorch.org/whl/cu113
# CPU only
conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cpuonly -c pytorch
# GPU 
conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge

the following presents a demo to use joyrl. As you can see, first create a yaml file to config hyperparameters, then run the command as below in your terminal. That's all you need to do to train a DQN agent on CartPole-v1 environment.

joyrl --yaml ./presets/ClassControl/CartPole-v1/CartPole-v1_DQN.yaml

or you can run the following code in your python file.

import joyrl
if __name__ == "__main__":
    print(joyrl.__version__)
    yaml_path = "./presets/ClassControl/CartPole-v1/CartPole-v1_DQN.yaml"
    joyrl.run(yaml_path = yaml_path)

More tutorials and API documentation are hosted on JoyRL docs or JoyRL 中文文档.

RL Platform	GitHub Stars	# of Alg. (1)	Custom Env	Async Training	RNN Support	Multi-Head Observation	Backend
Baselines		9	✔️ (gym)	❌	✔️	❌	TF1
Stable-Baselines		11	✔️ (gym)	❌	✔️	❌	TF1
Stable-Baselines3		7	✔️ (gym)	❌	❌	✔️	PyTorch
Ray/RLlib		16	✔️	✔️	✔️	✔️	TF/PyTorch
SpinningUp		6	✔️ (gym)	❌	❌	❌	PyTorch
Dopamine		7	❌	❌	❌	❌	TF/JAX
ACME		14	✔️ (dm_env)	❌	✔️	✔️	TF/JAX
keras-rl		7	✔️ (gym)	❌	❌	❌	Keras
cleanrl		9	✔️ (gym)	❌	❌	❌	poetry
rlpyt		11	❌	❌	✔️	✔️	PyTorch
ChainerRL		18	✔️ (gym)	❌	✔️	❌	Chainer
Tianshou		20	✔️ (Gymnasium)	❌	✔️	✔️	PyTorch
JoyRL		8	✔️ (Gymnasium)	✔️	✔️	✔️	PyTorch

Here are some other highlghts of JoyRL:

• Provide a series of Chinese courses JoyRL Book (with the English version in progress), suitable for beginners to start with a combination of theory

John Jim Peking University

Qi Wang Shanghai Jiao Tong University

Yiyuan Yang University of Oxford