PyTorch implementation of Soft Actor-Critic + Autoencoder(SAC+AE)

Implementation of Convolutional LSTM in PyTorch.

Pytorch Implement DRL algorithms (A2C, DDPG, PPO, TD3, SAC) for continuous action space control tasks.

Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

PPO x Family DRL Tutorial Course（决策智能入门级公开课：8节课帮你盘清算法理论，理顺代码逻辑，玩转决策AI应用实践 ）

Soft Actor-Critic

RLToolkit is a flexible and high-efficient reinforcement learning framework. Include implementation of DQN, AC,A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

This is a reinforcement learning algorithm library. The code takes into account both performance and simplicity, with little dependence.

PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) + Emphasizing Recent Experience (ERE) + Munchausen RL + D2RL and parallel Environments.

DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)

Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)

Deep Reinforcement Learning for Continuous Control in PyTorch

Diversity is All You Need: Learning Skills without a Reward Function in PyTorch.

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)