Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.
A PyTorch library for building deep reinforcement learning agents.
PyTorch implementations of algorithms from "Reinforcement Learning: An Introduction by Sutton and Barto", along with various RL research papers.
DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN
Deep Q-Learning (DQN) implementation for Atari pong.
A PyTorch Implementation of "Optimization of Molecules via Deep Reinforcement Learning".
A Torch Based RL Framework for Rapid Prototyping of Research Papers
Graph-based Deep Q Network for Web Navigation
SUMO Pytorch Deep Reinforcement Learning Traffic Signal Control
Important Note fastrl version 2 is being developed at fastrl. Note the link in the readme
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.
This code is the result of the collaboration of RL Turkey team.
Multi-agent reinforcement learning framework
Solving Atari Pong Game w/ Duel Double DQN in Pytorch
Reinforcement Learning for Optimal inventory policy
PyTorch agents and tools for (Deep) Reinforcement Learning
Reinforcement Learning - Implementation of Exercises, algorithms from the book Sutton Barto and David silver's RL course in Python, OpenAI Gym.
Implementation of Deep Reinforcement Learning algorithms in the Unity game engine.
Grid-scale li-ion battery optimisation for wholesale market arbitrage, using pytorch implementation of dqn, double dueling dqn and a noisy network dqn.
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