Reinforcement learning

Links

- βReinforcement Learning, An Introduction Book - Significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. (Web) (Code) (Julia Code) (Video Summary)
- βSpinning Up in Deep RL - Educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). (Docs) (HN) (Code)
- βDavid Silver Reinforcement learning - Notes for the Reinforcement Learning course by David Silver along with implementation of various algorithms.
- βRLlib - Open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications.
- βStable Baselines - Set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines.
- βpytorch-a3c - PyTorch implementation of Asynchronous Advantage Actor Critic (A3C) from "Asynchronous Methods for Deep Reinforcement Learning".
- βLearning to Paint - Painting AI that can reproduce paintings stroke by stroke using deep reinforcement learning.
- βRL Baselines Zoo - Collection of 100+ pre-trained RL agents using Stable Baselines, training and hyperparameter optimization included.
- βbsuite - Collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent.
- βOpenSpiel - Collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
- βStochastic Lower Bound Optimization - Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees.
- βBCQ - PyTorch implementation of BCQ for "Off-Policy Deep Reinforcement Learning without Exploration".
- βRLax - Library built on top of JAX that exposes useful building blocks for implementing reinforcement learning agents.
- βTransformer Reinforcement Learning - Train transformer language models with reinforcement learning.
- βReinforcement Learning Zoo - Collection of the most practical reinforcement learning algorithms, frameworks and applications.
- βmentalRL - A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry.
- βCoach - Python reinforcement learning framework containing implementation of many state-of-the-art algorithms.
- βSlime Volleyball Gym Environment - Simple OpenAI Gym environment for single and multi-agent reinforcement learning.
- βSURREAL - Fully integrated framework that runs state-of-the-art distributed reinforcement learning (RL) algorithms.
- βTF-Agents - Reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
- βStable Baselines3 - PyTorch version of Stable Baselines, improved implementations of reinforcement learning algorithms.
- βMulti-Agent Resource Optimization (MARO) - Instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization.
- βAI safety gridworlds - Suite of reinforcement learning environments illustrating various safety properties of intelligent agents.
- βTensorLayer - Deep Learning and Reinforcement Learning Library for Scientists and Engineers. (Docs)
- βAlpha Zero Boosted - "build to learn" implementation of the Alpha Zero algorithm written in Python that uses LightGBM (Gradient Boosted Decision Trees) in place of a Deep Neural Network for value/policy functions.
- βXingTian - Componentized library for the development and verification of reinforcement learning algorithms.
- βAddressing Function Approximation Error in Actor-Critic Methods - PyTorch implementation of Twin Delayed Deep Deterministic Policy Gradients (TD3).
- βDeepMind Lab2D - Flexible and fast engine for rapidly creating 2D environments. Built for RL, and well suited for the needs of multi-agent research. (Paper) (HN)
- βPettingZoo - Python library for conducting research in multi-agent reinforcement learning. It's akin to a multi-agent version of OpenAI's Gym library.
- βDeepMind Hard Eight Tasks - Set of 8 diverse machine-learning tasks that require exploration in partially observable environments to solve.
- βbanditml - Lightweight contextual bandit & reinforcement learning library designed to be used in production Python services.
- βSUMO-RL - Provides a simple interface to instantiate Reinforcement Learning environments with SUMO for Traffic Signal Control.
- βMuZero General - Commented and documented implementation of MuZero based on the Google DeepMind paper (Nov 2019) and the associated pseudocode.
- βReBeL - Algorithm that generalizes the paradigm of self-play reinforcement learning and search to imperfect-information games.
- βRLStructures - Library to facilitate the implementation of new reinforcement learning algorithms.
- βOpenAI PLE environment - Learning environment, mimicking the Arcade Learning Environment interface.
- βCleanRL - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features.
- βJax (Flax) RL - Jax (Flax) implementation of algorithms for Deep Reinforcement Learning with continuous action spaces.
- βAwesome Offline RL - Collection of research and review papers for offline reinforcement learning.
- βMeta-World - Open source robotics benchmark for meta- and multi-task reinforcement learning. (Web)
- βReinforcement Learning Examples - Policy Gradients, PPO+GAE, and DDQN Using OpenAI Gym and PyTorch.
- βrliable - Open-source library for reliable evaluation on reinforcement learning and machine learnings benchmarks.
- βPPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch.
- βSEED RL - Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.
- βFalken - Provides developers with a service that allows them to train AI that can play their games.
- βirl-imitation - Implementation of Inverse Reinforcement Learning (IRL) algorithms in python/Tensorflow. Deep MaxEnt, MaxEnt, LPIRL.
- βSaLinA: Sequential Learning of Agents (2021) - Flexible and Simple Library for Learning Sequential Agents (including Reinforcement Learning). (Code) (Tweet)
- βArcade Learning Environment (ALE) - Simple framework that allows researchers and hobbyists to develop AI agents for Atari 2600 games.
- βgym-hybrid - Collection of environment for reinforcement learning task possessing discrete-continuous hybrid action space.
- βRL Starter Files - RL starter files in order to immediately train, visualize and evaluate an agent without writing any line of code.
- βIsaac Gym Benchmark Environments - Contains example RL environments for the NVIDIA Isaac Gym high performance environments.
- βWarpDrive - Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU.
- βALF - Reinforcement learning framework emphasizing on the flexibility and easiness of implementing complex algorithms involving many different components.
- βGym-ANM - Design Reinforcement Learning environments that model Active Network Management (ANM) tasks in electricity distribution networks.
- βDeep Reinforcement Learning Toolkit for Cryptocurrencies - Record and replay cryptocurrency limit order book data & train a DDQN agent.
- βHandyRL - Handy and simple framework based on Python and PyTorch for distributed reinforcement learning that is applicable to your own environments.

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