openrl

OpenRL-Lab/openrl is ranked 13,269 among these 8 peers; LiyuanLucasLiu/LM-LSTM-CRF is ranked 13,268.

1 is updated on Dec 20, 2023 The main branch is the latest version of OpenRL, which is under active development. If you just want to have a try with OpenRL, you can switch to the stable branch. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. Currently, the features supported by OpenRL include: A **simple-to-use universal interface** that supports training

for all tasks/environments Support for both single-agent and multi-agent tasks Support for offline RL training with expert dataset Support self-play training Reinforcement learning training support for natural language tasks (such as dialogue) Support DeepSpeed Support Arena , which allows convenient evaluation of various agents (even submissions for JiDi) in a competitive environment. Importing models and datasets from Hugging Face. Supports loading Stable-baselines3 models from Hugging Face for testing and training. Tutorial on how to integrate user

-defined environments into OpenRL. Support for models such as LSTM, GRU, Transformer etc. Multiple training acceleration methods including automatic mixed precision training and data collecting wth half precision policy network User-defined training models, reward models, training data and environment support Support for gymnasium environments Support for Callbacks, which can be used to implement various functions such as logging, saving, and early stopping Dictionary observation space support Popular visualization tools such as wandb, tensorboardX are supported Serial or parallel environment

training while ensuring consistent results in both modes Chinese and English documentation Provides unit testing and code coverage testing Compliant with Black Code Style guidelines and type checking Algorithms currently supported by OpenRL (for more details, please refer to Gallery): Proximal Policy Optimization (PPO) Dual-clip PPO Multi-agent PPO (MAPPO) Joint-ratio Policy Optimization (JRPO) Generative Adversarial Imitation Learning (GAIL) Behavior Cloning (BC) Advantage Actor-Critic (A2C) Self-Play Deep Q-Network (DQN) Multi-Agent Transformer (MAT) Value-Decomposition

Network (VDN) Soft Actor Critic (SAC) Deep Deterministic Policy Gradient (DDPG) Environments currently supported by OpenRL (for more details, please refer to Gallery): Gymnasium MuJoCo PettingZoo MPE Chat Bot Atari StarCraft II SMACv2 Omniverse Isaac Gym DeepMind Control Snake gym-pybullet-drones EnvPool GridWorld Super Mario Bros Gym Retro Crafter This framework has undergone multiple iterations by the OpenRL-Lab team which has applied it in academic research. It has now become a mature reinforcement learning framework. OpenRL-Lab will

continue to maintain and update OpenRL, and we welcome everyone to join our open-source community to contribute towards the development of reinforcement learning. For more information about OpenRL, please refer to the documentation. Outline Welcome to OpenRL Outline Why OpenRL? Installation Use Docker Quick Start Gallery Projects Using OpenRL Feedback and Contribution Maintainers Supporters Citing OpenRL License Acknowledgments Why OpenRL Here we provide a table for the comparison of OpenRL and existing popular RL libraries. OpenRL employs a modular

design and high-level abstraction, allowing users to accomplish training for various tasks through a unified and user-friendly interface.