Popular GitHub Repositories for Td3

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. ?

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.

PyTorch implementation of deep reinforcement learning algorithms

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

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

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)

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

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

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.

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

TD3, SAC, IQN, Rainbow, PPO, Ape-X and etc. in TF1.x

Robot navigation using deep reinforcement learning

road-map & paper review for Reinforcement Learning