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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Learning Resources
- DeepMind’s RL Course - A comprehensive RL course by DeepMind researchers.
- Coursera: Reinforcement Learning Specialization - A series of courses on RL by the University of Alberta.
- Deep Reinforcement Learning Nanodegree (Udacity) - A program focused on deep RL techniques.
- David Silver’s Reinforcement Learning Course - A popular course by David Silver on RL concepts and algorithms.
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Core Algorithms
- SARSA (State-Action-Reward-State-Action) - An on-policy RL algorithm.
- Deep Q-Learning (DQN) - A value-based method using deep learning to approximate the Q-value function.
- 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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Frameworks and Libraries
- 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.
- TF-Agents - A library for reinforcement learning using TensorFlow.
- Dopamine - A research framework by Google focused on fast prototyping of RL algorithms.
- Acme - A library by DeepMind for building and testing reinforcement learning agents.
- Stable-Baselines3 - A reliable set of implementations of reinforcement learning algorithms in Python.
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Tools and Environments
- Unity ML-Agents - A toolkit by Unity for training intelligent agents using RL.
- PyBullet - An open-source Python module for physics simulations in RL.
- PettingZoo - A library of multi-agent reinforcement learning environments.
- 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.
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Advanced Algorithms
- Proximal Policy Optimization (PPO) - A stable and efficient policy optimization method.
- 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.
- A3C (Asynchronous Advantage Actor-Critic) - An efficient, asynchronous RL algorithm for training agents.
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Research Papers
- Playing Atari with Deep Reinforcement Learning (2013) - The seminal paper introducing DQN.
- Deep Reinforcement Learning with Double Q-learning (2016) - A paper that addresses the overestimation bias of Q-learning.
- Curiosity-driven Exploration by Self-supervised Prediction (2017) - A method for encouraging exploration in RL agents.
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Community
- Reddit: r/reinforcementlearning - A subreddit dedicated to discussions on RL research and applications.
- OpenAI Forum - A place to discuss OpenAI’s RL research and projects.
- 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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