awesome-reinforcement-learning
github.com/awesomelistsio/awesome-reinforcement-learning ↗A curated list of awesome frameworks, libraries, tools, environments, tutorials, research papers, and resources for reinforcement learning (RL). This list covers fundamental concepts, advanced algorithms, applications, and popular frameworks for building RL models.
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Frameworks and Libraries
- Acme
A library by DeepMind for building and testing reinforcement learning agents.
- Dopamine
A research framework by Google focused on fast prototyping of RL algorithms.
- OpenAI Baselines
A collection of high-quality implementations of RL algorithms by OpenAI.
- Ray RLlib
A scalable reinforcement learning library built on top of Ray.
- Stable-Baselines3
A reliable set of implementations of reinforcement learning algorithms in Python.
- TF-Agents
A library for reinforcement learning using TensorFlow.
Research Papers
- Asynchronous Methods for Deep Reinforcement Learning (2016)
The introduction of A3C, a highly efficient RL algorithm.
- Curiosity-driven Exploration by Self-supervised Prediction (2017)
A method for encouraging exploration in RL agents.
- Deep Reinforcement Learning with Double Q-learning (2016)
A paper that addresses the overestimation bias of Q-learning.
- Playing Atari with Deep Reinforcement Learning (2013)
The seminal paper introducing DQN.
- Proximal Policy Optimization Algorithms (2017)
The paper introducing PPO, a popular policy gradient method.
Tools and Environments
- CARLA Simulator
An open-source simulator for autonomous driving research using RL.
- DeepMind Control Suite
A set of Python-based reinforcement learning environments.
- OpenAI Gym
A toolkit for developing and comparing RL algorithms with a variety of environments.
- PettingZoo
A library of multi-agent reinforcement learning environments.
- PyBullet
An open-source Python module for physics simulations in RL.
- Unity ML-Agents
A toolkit by Unity for training intelligent agents using RL.
Learning Resources
- Coursera: Reinforcement Learning Specialization
A series of courses on RL by the University of Alberta.
- David Silver’s Reinforcement Learning Course
A popular course by David Silver on RL concepts and algorithms.
- DeepMind’s RL Course
A comprehensive RL course by DeepMind researchers.
- Deep Reinforcement Learning Nanodegree (Udacity)
A program focused on deep RL techniques.
Advanced Algorithms
- Deep Deterministic Policy Gradient (DDPG)
An off-policy algorithm for continuous action spaces.
- Soft Actor-Critic (SAC)
An entropy-regularized algorithm for stable learning in continuous action spaces.
- Trust Region Policy Optimization (TRPO)
An algorithm designed to maintain stable updates of the policy.
Community
- DeepMind Blog
A blog covering DeepMind’s latest research in RL.
- Discord: Reinforcement Learning Community
A Discord server for discussing RL topics.
- OpenAI Forum
A place to discuss OpenAI’s RL research and projects.
- Reddit: r/reinforcementlearning
A subreddit dedicated to discussions on RL research and applications.
- RLlib Users Group
A forum for discussing Ray’s RLlib.
Core Algorithms
- Deep Q-Learning (DQN)
A value-based method using deep learning to approximate the Q-value function.
- Policy Gradient Methods
A class of algorithms that directly optimize the policy.
- REINFORCE Algorithm
A Monte Carlo policy gradient method for training RL agents.
- SARSA (State-Action-Reward-State-Action)
An on-policy RL algorithm.
Showing a sample of 33 resources. View the full list on GitHub →