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.
https://github.com/awesomelistsio/awesome-reinforcement-learning
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Tools and Environments
- PyBullet - An open-source Python module for physics simulations in RL.
- CARLA Simulator - An open-source simulator for autonomous driving research using RL.
- OpenAI Gym - A toolkit for developing and comparing RL algorithms with a variety of environments.
- Unity ML-Agents - A toolkit by Unity for training intelligent agents using RL.
- PettingZoo - A library of multi-agent reinforcement learning environments.
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Advanced Algorithms
- Proximal Policy Optimization (PPO) - A stable and efficient policy optimization method.
- Trust Region Policy Optimization (TRPO) - An algorithm designed to maintain stable updates of the policy.
- Soft Actor-Critic (SAC) - An entropy-regularized algorithm for stable learning in continuous action spaces.
- A3C (Asynchronous Advantage Actor-Critic) - An efficient, asynchronous RL algorithm for training agents.
- Deep Deterministic Policy Gradient (DDPG) - An off-policy algorithm for continuous action spaces.
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Research Papers
- 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.
- Curiosity-driven Exploration by Self-supervised Prediction (2017) - A method for encouraging exploration in RL agents.
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Learning Resources
- Deep Reinforcement Learning Nanodegree (Udacity) - A program focused on deep RL techniques.
- Coursera: Reinforcement Learning Specialization - A series of courses on RL by the University of Alberta.
- DeepMind’s RL Course - A comprehensive RL course by DeepMind researchers.
- David Silver’s Reinforcement Learning Course - A popular course by David Silver on RL concepts and algorithms.
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Frameworks and Libraries
- TF-Agents - A library for reinforcement learning using TensorFlow.
- 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.
- Acme - A library by DeepMind for building and testing reinforcement learning agents.
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Community
- 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.
- Discord: Reinforcement Learning Community - A Discord server for discussing RL topics.
- RLlib Users Group - A forum for discussing Ray’s RLlib.
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Core Algorithms
- Deep Q-Learning (DQN) - A value-based method using deep learning to approximate the Q-value function.
- SARSA (State-Action-Reward-State-Action) - An on-policy RL algorithm.
- REINFORCE Algorithm - A Monte Carlo policy gradient method for training RL agents.
- Policy Gradient Methods - A class of algorithms that directly optimize the policy.
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