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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
Last synced: 4 days ago
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Frameworks and Libraries
- Ray RLlib - A scalable reinforcement learning library built on top of Ray.
- Dopamine - A research framework by Google focused on fast prototyping of RL algorithms.
- TF-Agents - A library for reinforcement learning using TensorFlow.
- OpenAI Baselines - A collection of high-quality implementations of RL algorithms by OpenAI.
- TF-Agents - A library for reinforcement learning using TensorFlow.
- Ray RLlib - A scalable reinforcement learning library built on top of Ray.
- OpenAI Baselines - A collection of high-quality implementations of RL algorithms by OpenAI.
- Dopamine - A research framework by Google focused on fast prototyping of RL algorithms.
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Tools and Environments
- 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.
- 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.
- 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.
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Core Algorithms
- 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.
- 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.
- Actor-Critic Methods - Algorithms that use both policy (actor) and value (critic) functions.
- REINFORCE Algorithm - A Monte Carlo policy gradient method for training RL agents.
- SARSA (State-Action-Reward-State-Action) - An on-policy RL algorithm.
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Advanced Algorithms
- A3C (Asynchronous Advantage Actor-Critic) - An efficient, asynchronous RL algorithm for training agents.
- 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.
- 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.
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Learning Resources
- 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.
- DeepMind’s RL Course - A comprehensive RL course by DeepMind researchers.
- Deep Reinforcement Learning Nanodegree (Udacity) - A program focused on deep RL techniques.
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