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)

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

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

Prioritized Experience Replay (PER) implementation in PyTorch

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

Repository for codes of 'Deep Reinforcement Learning'

DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN

Mapless Collision Avoidance of Turtlebot3 Mobile Robot Using DDPG and Prioritized Experience Replay

PyTorch Implementation of Implicit Quantile Networks (IQN) for Distributional Reinforcement Learning with additional extensions like PER, Noisy layer, N-step bootstrapping, Dueling architecture and parallel env support.

Pytorch implementation of distributed deep reinforcement learning

Prioritized Experience Replay implementation with proportional prioritization

A Torch Based RL Framework for Rapid Prototyping of Research Papers

强化学习算法库，包含了目前主流的强化学习算法(Value based and Policy based)的代码，代码都经过调试并可以运行

A novel DDPG method with prioritized experience replay (IEEE SMC 2017)

Implementation of Deep Deterministic Policy Gradient (DDPG) with Prioritized Experience Replay (PER)

This Repository contains a series of google colab notebooks which I created to help people dive into deep reinforcement learning.This notebooks contain both theory and implementation of different algorithms.

PyTorch implementation of the Q-Learning Algorithm Normalized Advantage Function for continuous control problems + PER and N-step Method

PyTorch implementation of the state-of-the-art distributional reinforcement learning algorithm Fully Parameterized Quantile Function (FQF) and Extensions: N-step Bootstrapping, PER, Noisy Layer, Dueling Networks, and parallelization.

RLCodebase: PyTorch Codebase For Deep Reinforcement Learning Algorithms