There are 1 repository under td3 topic.
强化学习中文教程(蘑菇书🍄),在线阅读地址:https://datawhalechina.github.io/easy-rl/
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 DRL Algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
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
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.
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC)
Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math
Simple (but often Strong) Baselines for POMDPs in PyTorch, ICML 2022
Autonomous UAV Navigation without Collision using Visual Information in Airsim
Paddle-RLBooks is a reinforcement learning code study guide based on pure PaddlePaddle.
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
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
This is a reinforcement learning algorithm library. The code takes into account both performance and simplicity, with little dependence.
JAX implementations of core Deep RL algorithms
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
road-map & paper review for Reinforcement Learning
Robot navigation using deep reinforcement learning
The implement of the policy gradient RL algorithm with pytorch
Benchmark data (i.e., DeepMind Control Suite and MuJoCo) for RL.
Mxnet implementation of Deep Reinforcement Learning papers, such as DQN, PG, DDPG, PPO
RLCodebase: PyTorch Codebase For Deep Reinforcement Learning Algorithms