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An easier PyTorch deep reinforcement learning library.
| Date | Stars |
|---|---|
| 2026-07-31 | 261 |
| 2026-08-02 | 261 |
| 2026-08-03 | 261 |
| 2026-08-06 | 261 |
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# JoyRL
[](https://pypi.org/project/joyrl/) [](https://github.com/datawhalechina/joyrl/issues) [](https://github.com/datawhalechina/joyrl/stargazers) [](https://github.com/datawhalechina/joyrl/network) [](https://github.com/datawhalechina/joyrl/blob/master/LICENSE)
`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.
## Install
⚠️ Note that donot install JoyRL through any mirror image!!!
```bash
# you need to install Anaconda first
conda create -n joyrl python=3.10
conda activate joyrl
pip install -U joyrl
```
Torch install:
```bash
# CPU
pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1
# CUDA 11.8
pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu118
# CUDA 12.1
pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu121
```
## Usage
### Quick Start
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.
```bash
joyrl --yaml ./presets/ClassControl/CartPole-v1/CartPole-v1_DQN.yaml
```
or you can run the following code in your python file.
```python
import joyrl
if __name__ == "__main__":
print(joyrl.__version__)
yaml_path = "./presets/ClassControl/CartPole-v1/CartPole-v1_DQN.yaml"
joyrl.run(yaml_path = yaml_path)
```
## Documentation
More tutorials and API documentation are hosted on [JoyRL docs](https://datawhalechina.github.io/joyrl/) or [JoyRL 中文文档](https://datawhalechina.github.io/joyrl-book/#/joyrl_docs/main).
## Algorithms
| Name | Reference | Author | Notes |
| :--------------: | :----------------------------------------------------------: | :-------------------------------------------: | :---: |
| Q-learning | [RL introduction](https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf) | [johnjim0816](https://github.com/johnjim0816) | |
| Sarsa | [RL introduction](https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf) | [johnjim0816](https://github.com/johnjim0816) | |
| DQN | [DQN Paper](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf) | [johnjim0816](https://github.com/johnjim0816) | |
| Double DQN | [DoubleDQN Paper](https://arxiv.org/abs/1509.06461) | [johnjim0816](https://github.com/johnjim0816) | |
| Dueling DQN | [DuelingDQN Paper](https://arxiv.org/abs/1511.06581) | [johnjim0816](https://github.com/johnjim0816) | |
| NoisyDQN | [NoisyDQN Paper](https://arxiv.org/pdf/1706.10295.pdf) | [johnjim0816](https://github.com/johnjim0816) | |
| CategoricalDQN | [CategoricalDQN Paper](https://arxiv.org/abs/1707.06887) | [johnjim0816](https://github.com/johnjim0816) | |
| DDPG | [DDPG Paper](https://arxiv.org/abs/1509.02971) | [johnjim0816](https://github.com/johnjim0816) | |
| TD3 | [TD3 Paper](https://arxiv.org/pdf/1802.09477) | [johnjExcerpt of 10,841 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c6e2602b1f793cb3, llm:Repository description: 'An easier PyTorch deep reinforcement learning library.' Language: Python. No topics provided.
matched fp:c6e2602b1f793cb3, llm:Repository description: 'An easier PyTorch deep reinforcement learning library.' Language: Python. No topics provided.
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