A simple and well styled PPO implementation. Based on my Medium series: https://medium.com/@eyyu/coding-ppo-from-scratch-with-pytorch-part-1-4-613dfc1b14c8.

An elegant PyTorch deep reinforcement learning library.

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/

Tools for accelerating safe exploration research.

Hybrid CPU/GPU implementation of the A3C algorithm for deep reinforcement learning.

Python Multi-Agent Reinforcement Learning framework

Distributed RL platform with modified IMPALA architecture. Implements CLEAR, LASER V-trace modifications along with Attentive and Elite sampling experience replay methods.

PyTorch IMPALA implementation

A PyTorch Platform for Distributed RL

SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.

Distributed RL Implementation using Pytorch and Ray (ApeX(Ape-X), A3C, Distributed-PPO(DPPO), Impala)

A TensorFlow implementation of Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures.

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.