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

An elegant PyTorch deep reinforcement learning library.

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

Massively Parallel Deep Reinforcement Learning. 🔥

Modularized Implementation of Deep RL Algorithms in PyTorch

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin Delayed Deep Deterministic Policy Gradient (TD3) neural network, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.

PyTorch implementation of deep reinforcement learning algorithms

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.

Machine Readable Zone generator and checker for official travel documents sizes 1, 2, 3, MRVA and MRVB (Passports, Visas, national id cards and other travel documents)

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

Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022

Deep Reinforcement Learning for mobile robot navigation in IR-SIM simulation. Using DRL (SAC, TD3, PPO, DDPG) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

Autonomous UAV Navigation without Collision using Visual Information in Airsim

Pytorch Implementation of Reinforcement Learning Algorithms ( Soft Actor Critic(SAC)/ DDPG / TD3 /DQN / A2C/ PPO / TRPO)

Deep Reinforcement Learning for mobile robot navigation in ROS2 Gazebo simulator. Using DRL (SAC, TD3) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

Paddle-RLBooks is a reinforcement learning code study guide based on pure PaddlePaddle.

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

Twin Delayed DDPG (TD3) PyTorch solution for Roboschool and Box2d environment

Implementation of Algorithms from the Policy Gradient Family. Currently includes: A2C, A3C, DDPG, TD3, SAC

Robot arm control using reinforcement learning algorithms : DDPG and TD3 with hindsight experience replay (HER)

Basic reinforcement learning algorithms. Including:DQN,Double DQN, Dueling DQN, SARSA, REINFORCE, baseline-REINFORCE, Actor-Critic,DDPG,DDPG for discrete action space, A2C, A3C, TD3, SAC, TRPO

Accelerating Quadratic Optimization with Reinforcement Learning

Code for the RL method MATD3 described in the paper "Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics"

JAX implementations of core Deep RL algorithms

A Torch Based RL Framework for Rapid Prototyping of Research Papers

Robot navigation using deep reinforcement learning