OpenRL Introduction

OpenRL Reinforcement Learning Framework

OpenRL is a reinforcement learning research framework based on PyTorch developed by the Reinforcement Learning Team of 4Paradigm. It provides a simple and easy-to-use interface that allows you to easily access different reinforcement learning environments. Currently, OpenRL framework has the following features:

1. Simple and easy-to-use training interface, reducing the learning and usage costs of researchers.

2. Supports both single-agent and multi-agent algorithms.

3. Supports reinforcement learning training for natural language tasks (such as dialogue tasks).

4. Supports model import from Hugging Face.

5. Supports models such as LSTM, GRU, Transformer, etc.

6. Supports various training accelerations, such as mixed precision training, data collecting with half-precision policy network, etc.

7. Support gymnasium environments.

8. Support dictionary-type observation input.

9. Support popular machine learning training visualization platforms such as wandb and tensorboardX .

10. Supports serial and parallel training of environments while ensuring consistent performance under both scenarios.

11. Provides code coverage testing and unit testing.

In the following section on Quick Start , we will introduce how to install the OpenRL framework, and demonstrate how to use OpenRL through simple examples.

Users can also check the algorithms and environments supported by OpenRL, as well as obtain corresponding code in the Gallery.

Citing OpenRL

If our work is helpful to you, please cite us:

@misc{openrl2023,
    title={OpenRL},
    author={OpenRL Contributors},
    publisher = {GitHub},
    howpublished = {\url{https://github.com/OpenRL-Lab/openrl}},
    year={2023},
}