{"id":7200,"url":"https://github.com/kengz/awesome-deep-rl","name":"awesome-deep-rl","description":"A curated list of awesome Deep Reinforcement Learning resources.","projects_count":215,"last_synced_at":"2026-10-17T16:00:24.177Z","repository":{"id":37525916,"uuid":"202937615","full_name":"kengz/awesome-deep-rl","owner":"kengz","description":"A curated list of awesome Deep Reinforcement Learning resources.","archived":false,"fork":false,"pushed_at":"2025-07-13T16:02:20.000Z","size":142,"stargazers_count":903,"open_issues_count":3,"forks_count":88,"subscribers_count":30,"default_branch":"master","last_synced_at":"2026-10-17T12:15:53.087Z","etag":null,"topics":["awesome-list","deep-reinforcement-learning","deep-rl","reinforcement-learning","resources"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kengz.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-08-17T22:43:43.000Z","updated_at":"2026-10-14T07:10:15.000Z","dependencies_parsed_at":"2024-01-12T17:34:24.000Z","dependency_job_id":"f2c0617a-91cb-4f5f-a572-4a22be7cadf1","html_url":"https://github.com/kengz/awesome-deep-rl","commit_stats":{"total_commits":37,"total_committers":10,"mean_commits":3.7,"dds":0.2432432432432432,"last_synced_commit":"8005f930c89c85552e2b0f35d3a86bd2f8017b3e"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/kengz/awesome-deep-rl","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kengz%2Fawesome-deep-rl","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kengz%2Fawesome-deep-rl/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kengz%2Fawesome-deep-rl/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kengz%2Fawesome-deep-rl/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kengz","download_url":"https://codeload.github.com/kengz/awesome-deep-rl/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kengz%2Fawesome-deep-rl/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":344872684,"owners_count":38334320,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-10-03T21:59:58.778Z","status":"online","status_checked_at":"2026-10-17T02:00:06.547Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-12T13:37:35.971Z","updated_at":"2026-10-17T16:00:24.178Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Timeline","Tutorials","Blogs","Benchmark Results","Environments","Competitions","Books","Libraries"],"sub_categories":[],"readme":"# Awesome Deep RL [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)\nA curated list of awesome Deep Reinforcement Learning resources.\n\n## Contents\n\n- [Libraries](#libraries)\n- [Benchmark Results](#benchmark-results)\n- [Environments](#environments)\n- [Competitions](#competitions)\n- [Timeline](#timeline)\n- [Books](#books)\n- [Tutorials](#tutorials)\n- [Blog](#blogs)\n\n## Libraries\n\n- [AgileRL](https://github.com/AgileRL/AgileRL) - A Deep Reinforcement Learning library focused on improving development by introducing RLOps - MLOps for reinforcement learning.\n- [Berkeley Ray RLLib](https://github.com/ray-project/ray) - An open-source library for reinforcement learning that offers both high scalability and a unified API for a variety of applications.\n- [Berkeley Softlearning](https://github.com/rail-berkeley/softlearning) - A reinforcement learning framework for training maximum entropy policies in continuous domains.\n- [Catalyst](https://github.com/catalyst-team/catalyst) - Accelerated DL \u0026 RL.\n- [ChainerRL](https://github.com/chainer/chainerrl) - A deep reinforcement learning library built on top of Chainer.\n- [DeepMind Acme](https://github.com/deepmind/acme) - A research framework for reinforcement learning.\n- [DeepMind OpenSpiel](https://github.com/deepmind/open_spiel) - A collection of environments and algorithms for research in general reinforcement learning and search/planning in games.\n- [DeepMind TRFL](https://github.com/deepmind/trfl) - TensorFlow Reinforcement Learning.\n- [DeepRL](https://github.com/ShangtongZhang/DeepRL) - Modularized Implementation of Deep RL Algorithms in PyTorch.\n- [DeepX machina](https://github.com/DeepX-inc/machina) - A library for real-world Deep Reinforcement Learning which is built on top of PyTorch.\n- [d3rlpy](https://github.com/takuseno/d3rlpy) - An offline deep reinforcement learning library.\n- [Facebook ELF](https://github.com/pytorch/ELF) - A platform for game research with AlphaGoZero/AlphaZero reimplementation.\n- [Facebook ReAgent](https://github.com/facebookresearch/ReAgent) - A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)\n- [garage](https://github.com/rlworkgroup/garage) - A toolkit for reproducible reinforcement learning research.\n- [Google Dopamine](https://github.com/google/dopamine) - A research framework for fast prototyping of reinforcement learning algorithms.\n- [Google TF-Agents](https://github.com/tensorflow/agents) - TF-Agents is a library for Reinforcement Learning in TensorFlow.\n- [K-Scale Labs - ksim](https://github.com/kscalelabs/ksim) - A modular and easy-to-use framework for training policies in simulation.\n- [K-Scale Labs - ksim-gym](https://github.com/kscalelabs/ksim-gym) - K-Sim Gym: Making robots useful with RL. Built on top of K-Sim.\n- [MAgent](https://github.com/geek-ai/MAgent) - A Platform for Many-agent Reinforcement Learning.\n- [Maze](https://github.com/enlite-ai/maze) - Application-oriented deep reinforcement learning framework addressing real-world decision problems.\n- [MushroomRL](https://github.com/MushroomRL/mushroom-rl) - Python library for Reinforcement Learning experiments.\n- [NervanaSystems coach](https://github.com/NervanaSystems/coach) - Reinforcement Learning Coach by Intel AI Lab.\n- [OpenAI Baselines](https://github.com/openai/baselines) - High-quality implementations of reinforcement learning algorithms.\n- [OpenRL](https://github.com/OpenRL-Lab/openrl) - An open-source general reinforcement learning research framework.\n- [pytorch-a2c-ppo-acktr-gail](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail) - PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).\n- [pytorch-rl](https://github.com/navneet-nmk/pytorch-rl) - Model-free deep reinforcement learning algorithms implemented in Pytorch.\n- [reaver](https://github.com/inoryy/reaver) - A modular deep reinforcement learning framework with a focus on various StarCraft II based tasks.\n- [RLgraph](https://github.com/rlgraph/rlgraph) - Modular computation graphs for deep reinforcement learning.\n- [RLkit](https://github.com/vitchyr/rlkit) - Reinforcement learning framework and algorithms implemented in PyTorch.\n- [rlpyt](https://github.com/astooke/rlpyt) - Reinforcement Learning in PyTorch.\n- [RLtools](https://github.com/rl-tools/rl-tools) - The fastest deep reinforcement learning library for continuous control, implemented in pure, dependency-free C++ (Python bindings available as well).\n- [skrl](https://github.com/Toni-SM/skrl) - Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Omniverse Isaac Gym and Isaac Lab.\n- [SLM Lab](https://github.com/kengz/SLM-Lab) - Modular Deep Reinforcement Learning framework in PyTorch.\n- [Stable Baselines](https://github.com/hill-a/stable-baselines) - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms.\n- [TensorForce](https://github.com/tensorforce/tensorforce) - A TensorFlow library for applied reinforcement learning.\n- [Tianshou](https://github.com/thu-ml/tianshou/) - Tianshou (天授) is a reinforcement learning platform based on pure PyTorch.\n- [TorchRL](https://docs.pytorch.org/rl/stable/index.html) - An open-source Reinforcement Learning (RL) library for PyTorch.\n- [UMass Amherst Autonomous Learning Library](https://github.com/cpnota/autonomous-learning-library) - A PyTorch library for building deep reinforcement learning agents.\n- [Unity ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents) - Unity Machine Learning Agents Toolkit.\n- [vel](https://github.com/MillionIntegrals/vel) - Bring velocity to deep-learning research.\n- [DI-engine](https://github.com/opendilab/DI-engine) - A generalized decision intelligence engine. It supports various Deep RL algorithms.\n\n## Benchmark Results\n\n- [DeepMind bsuite](https://github.com/deepmind/bsuite/tree/master/bsuite)\n- [OpenAI baselines-results](https://github.com/openai/baselines-results)\n- [OpenAI Baselines](https://github.com/openai/baselines#benchmarks)\n- [OpenAI Spinning Up](https://spinningup.openai.com/en/latest/spinningup/bench.html)\n- [ray rl-experiments](https://github.com/ray-project/rl-experiments)\n- [rl-baselines-zoo](https://github.com/araffin/rl-baselines-zoo/blob/master/benchmark.md)\n- [SLM Lab](https://github.com/kengz/SLM-Lab/blob/master/BENCHMARK.md)\n- [vel](https://blog.millionintegrals.com/vel-pytorch-meets-baselines)\n- [What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study](https://arxiv.org/abs/2006.05990)\n- [yarlp](https://github.com/btaba/yarlp)\n\n## Environments\n\n- [AI2-THOR](https://github.com/allenai/ai2thor) - A near photo-realistic interactable framework for AI agents.\n- [Animal-AI Olympics](https://github.com/beyretb/AnimalAI-Olympics) - An AI competition with tests inspired by animal cognition.\n- [Berkeley rl-generalization](https://github.com/sunblaze-ucb/rl-generalization) - Modifiable OpenAI Gym environments for studying generalization in RL.\n- [BTGym](https://github.com/Kismuz/btgym) - Scalable event-driven RL-friendly backtesting library. Build on top of Backtrader with OpenAI Gym environment API.\n- [Carla](https://github.com/carla-simulator/carla) - Open-source simulator for autonomous driving research.\n- [CuLE](https://github.com/NVlabs/cule) - A CUDA port of the Atari Learning Environment (ALE).\n- [Deepdrive](https://github.com/deepdrive/deepdrive) - End-to-end simulation for self-driving cars.\n- [DeepMind AndroidEnv](https://github.com/deepmind/android_env) - A library for doing RL research on Android devices.\n- [DeepMind DM Control](https://github.com/deepmind/dm_control) - The DeepMind Control Suite and Package.\n- [DeepMind Lab](https://github.com/deepmind/lab) - A customisable 3D platform for agent-based AI research.\n- [DeepMind pycolab](https://github.com/deepmind/pycolab) - A highly-customisable gridworld game engine with some batteries included.\n- [DeepMind PySC2](https://github.com/deepmind/pysc2) - StarCraft II Learning Environment.\n- [DeepMind RL Unplugged](https://github.com/deepmind/deepmind-research/tree/master/rl_unplugged) - Benchmarks for Offline Reinforcement Learning.\n- [Facebook EmbodiedQA](https://github.com/facebookresearch/EmbodiedQA) - Train embodied agents that can answer questions in environments.\n- [Facebook Habitat](https://github.com/facebookresearch/habitat-api) - A modular high-level library to train embodied AI agents across a variety of tasks, environments, and simulators.\n- [Facebook House3D](https://github.com/facebookresearch/House3D) - A Rich and Realistic 3D Environment.\n- [Facebook natural_rl_environment](https://github.com/facebookresearch/natural_rl_environment) - natural signal Atari environments, introduced in the paper Natural Environment Benchmarks for Reinforcement Learning.\n- [Google Research Football](https://github.com/google-research/football) - An RL environment based on open-source game Gameplay Football.\n- [GVGAI Gym](https://github.com/rubenrtorrado/GVGAI_GYM) - An OpenAI Gym environment for games written in the Video Game Description Language, including the Generic Video Game Competition framework.\n- [gym-doom](https://github.com/ppaquette/gym-doom) - Doom environments based on VizDoom.\n- [gym-duckietown](https://github.com/duckietown/gym-duckietown) - Self-driving car simulator for the Duckietown universe.\n- [gym-gazebo2](https://github.com/AcutronicRobotics/gym-gazebo2) - A toolkit for developing and comparing reinforcement learning algorithms using ROS 2 and Gazebo.\n- [gym-ignition](https://github.com/robotology/gym-ignition) - Experimental OpenAI Gym environments implemented with Ignition Robotics.\n- [gym-idsgame](https://github.com/Limmen/gym-idsgame) - An Abstract Cyber Security Simulation and Markov Game for OpenAI Gym\n- [gym-super-mario](https://github.com/ppaquette/gym-super-mario) - 32 levels of original Super Mario Bros.\n- [Gym4ReaL](https://github.com/Daveonwave/gym4ReaL) - A Gymnasium-based benchmarking suite for testing reinforcement learning algorithms on real-world scenarios, including water management, energy management in microgrids, financial trading, and more.\n- [Holodeck](https://github.com/BYU-PCCL/holodeck) - High Fidelity Simulator for Reinforcement Learning and Robotics Research.\n- [home-platform](https://github.com/HoME-Platform/home-platform) - A platform for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context\n- [ma-gym](https://github.com/koulanurag/ma-gym) - A collection of multi agent environments based on OpenAI gym.\n- [mazelab](https://github.com/zuoxingdong/mazelab) - A customizable framework to create maze and gridworld environments.\n- [Meta-World](https://github.com/rlworkgroup/metaworld) - An open source robotics benchmark for meta- and multi-task reinforcement learning.\n- [Microsoft AirSim](https://github.com/Microsoft/AirSim) - Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI \u0026 Research.\n- [Microsoft Jericho](https://github.com/microsoft/jericho) - A learning environment for man-made Interactive Fiction games.\n- [Microsoft Malmö](https://github.com/Microsoft/malmo) - A platform for Artificial Intelligence experimentation and research built on top of Minecraft.\n- [Microsoft MazeExplorer](https://github.com/microsoft/MazeExplorer) - Customisable 3D environment for assessing generalisation in Reinforcement Learning.\n- [Microsoft TextWorld](https://github.com/microsoft/TextWorld) - A text-based game generator and extensible sandbox learning environment for training and testing reinforcement learning (RL) agents.\n- [MineRL](https://github.com/minerllabs/minerl) - MineRL Competition for Sample Efficient Reinforcement Learning.\n- [MuJoCo](http://www.mujoco.org) - Advanced physics simulation.\n- [OpenAI Coinrun](https://github.com/openai/coinrun) - Code for the environments used in the paper Quantifying Generalization in Reinforcement Learning.\n- [OpenAI Gym Retro](https://github.com/openai/retro) - Retro Games in Gym.\n- [OpenAI Gym Soccer](https://github.com/openai/gym-soccer) - A multiagent domain featuring continuous state and action spaces.\n- [OpenAI Gym](https://github.com/openai/gym) - A toolkit for developing and comparing reinforcement learning algorithms.\n- [OpenAI Multi-Agent Particle Environment](https://github.com/openai/multiagent-particle-envs) - A simple multi-agent particle world with a continuous observation and discrete action space, along with some basic simulated physics.\n- [OpenAI Neural MMO](https://github.com/openai/neural-mmo) - A Massively Multiagent Game Environment.\n- [OpenAI Procgen Benchmark](https://github.com/openai/procgen) - Procedurally Generated Game-Like Gym Environments.\n- [OpenAI Roboschool](https://github.com/openai/roboschool) - Open-source software for robot simulation, integrated with OpenAI Gym.\n- [OpenAI RoboSumo](https://github.com/openai/robosumo) - A set of competitive multi-agent environments used in the paper Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments.\n- [OpenAI Safety Gym](https://github.com/openai/safety-gym) - Tools for accelerating safe exploration research.\n- [Personae](https://github.com/Ceruleanacg/Personae) - RL \u0026 SL Methods and Envs For Quantitative Trading.\n- [Pommerman](https://github.com/MultiAgentLearning/playground) - A clone of Bomberman built for AI research.\n- [pybullet-gym](https://github.com/benelot/pybullet-gym) - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform\n- [PyGame Learning Environment](https://github.com/ntasfi/PyGame-Learning-Environment) - Reinforcement Learning Environment in Python.\n- [RLBench](https://github.com/stepjam/RLBench) - A large-scale benchmark and learning environment.\n- [RLGym](https://github.com/lucas-emery/rocket-league-gym) - A python API to treat the game Rocket League as an OpenAI Gym environment.\n- [RLTrader](https://github.com/notadamking/RLTrader) - A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym.\n- [RoboNet](https://blog.ml.cmu.edu/2019/11/26/robonet/) - A Dataset for Large-Scale Multi-Robot Learning.\n- [rocket-lander](https://github.com/arex18/rocket-lander) - SpaceX Falcon 9 Box2D continuous-action simulation with traditional and AI controllers.\n- [Stanford Gibson Environments](https://github.com/StanfordVL/GibsonEnv) - Real-World Perception for Embodied Agents.\n- [Stanford osim-rl](https://github.com/stanfordnmbl/osim-rl) - Reinforcement learning environments with musculoskeletal models.\n- [Unity ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents) - Unity Machine Learning Agents Toolkit.\n- [UnityObstableTower](https://github.com/Unity-Technologies/obstacle-tower-env) - A procedurally generated environment consisting of multiple floors to be solved by a learning agent.\n- [VizDoom](https://github.com/mwydmuch/ViZDoom) - Doom-based AI Research Platform for Reinforcement Learning from Raw Visual Information.\n- [RLCard](https://github.com/datamllab/rlcard/) - A research platform for reinforcement learning in card games.\n- [DouZero](https://github.com/kwai/DouZero/) - A research platform for reinforcement learning in DouDizhu (Chinese poker).\n\n\n## Competitions\n\n- [AWS DeepRacer League 2019](https://aws.amazon.com/deepracer/league/)\n- [Flatland Challenge 2019](https://www.aicrowd.com/challenges/flatland-challenge)\n- [Kaggle Connect X Competition 2020](https://www.kaggle.com/c/connectx)\n- [NeurIPS 2019: Animal-AI Olympics](http://animalaiolympics.com/)\n- [NeurIPS 2019: Game of Drones](https://www.microsoft.com/en-us/research/academic-program/game-of-drones-competition-at-neurips-2019/)\n- [NeurIPS 2019: Learn to Move - Walk Around](https://www.aicrowd.com/challenges/neurips-2019-learning-to-move-walk-around)\n- [NeurIPS 2019: MineRL Competition](http://minerl.io/competition/)\n- [NeurIPS 2019: Reconnaissance Blind Chess](https://rbc.jhuapl.edu/)\n- [NeurIPS 2019: Robot open-Ended Autonomous Learning](https://www.aicrowd.com/challenges/robot-open-ended-autonomous-learning-real)\n- [Unity Obstacle Tower Challenge 2019](https://blogs.unity3d.com/2019/01/28/obstacle-tower-challenge-test-the-limits-of-intelligence-systems/)\n\n\u003eCheck [AICrowd](https://www.aicrowd.com) for the latest list of major RL competitions\n\n## Timeline\n\n- 1947: [Monte Carlo Sampling](http://eniacinaction.com/the-articles/3-los-alamos-bets-on-eniac-nuclear-monte-carlo-simulations-1947-8/)\n- 1958: [Perceptron](https://www.ling.upenn.edu/courses/cogs501/Rosenblatt1958.pdf)\n- 1959: [Temporal Difference Learning](https://dl.acm.org/citation.cfm?id=1661924)\n- 1983: [ASE-ALE — the first Actor-Critic algorithm](https://psycnet.apa.org/record/1984-13799-001)\n- 1986: [Backpropagation algorithm](https://www.nature.com/articles/323533a0)\n- 1989: [CNNs](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.479\u0026rep=rep1\u0026type=pdf)\n- 1989: [Q-Learning](http://www.cs.rhul.ac.uk/~chrisw/new_thesis.pdf)\n- 1991: [TD-Gammon](http://bkgm.com/books/Robertie-LearningFromTheMachine.html)\n- 1992: [REINFORCE](https://dl.acm.org/citation.cfm?id=139614)\n- 1992: [Experience Replay](https://dl.acm.org/citation.cfm?id=139620)\n- 1994: [SARSA](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.17.2539)\n- 1999: [Nvidia invented the GPU](https://www.nvidia.com/object/gpu.html)\n- 2007: [CUDA released](https://developer.nvidia.com/cuda-zone)\n- 2012: [Arcade Learning Environment (ALE)](https://arxiv.org/abs/1207.4708)\n- 2013: [DQN](https://arxiv.org/abs/1312.5602)\n- 2015 Feb: [DQN human-level control in Atari](https://www.nature.com/articles/nature14236)\n- 2015 Feb: [TRPO](https://arxiv.org/abs/1502.05477)\n- 2015 Jun: [Generalized Advantage Estimation](https://arxiv.org/abs/1506.02438)\n- 2015 Sep: [Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/abs/1509.02971)\n- 2015 Sep: [DoubleDQN](https://arxiv.org/abs/1509.06461)\n- 2015 Nov: [DuelingDQN](https://arxiv.org/abs/1511.06581)\n- 2015 Nov: [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952)\n- 2015 Nov: [TensorFlow](https://www.tensorflow.org/)\n- 2016 Feb: [A3C](https://arxiv.org/abs/1602.01783)\n- 2016 Mar: [AlphaGo beats Lee Sedol 4-1](https://deepmind.com/alphago-korea)\n- 2016 Jun: [OpenAI Gym](https://github.com/openai/gym)\n- 2016 Jun: [Generative Adversarial Imitation Learning (GAIL)](https://arxiv.org/abs/1606.03476)\n- 2016 Oct: [PyTorch](https://pytorch.org/)\n- 2017 Mar: [Model-Agnostic Meta-Learning (MAML)](https://arxiv.org/abs/1703.03400)\n- 2017 Jul: [Distributional RL](https://arxiv.org/abs/1707.06887)\n- 2017 Jul: [PPO](https://arxiv.org/abs/1707.06347)\n- 2017 Aug: [OpenAI DotA 2 1:1](https://openai.com/blog/more-on-dota-2/)\n- 2017 Aug: [Intrinsic Cusiority Module (ICM)](https://arxiv.org/abs/1705.05363)\n- 2017 Oct: [Rainbow](https://arxiv.org/abs/1710.02298)\n- 2017 Oct: [AlphaGo Zero masters Go without human knowledge](https://deepmind.com/blog/article/alphago-zero-starting-scratch)\n- 2017 Dec: [AlphaZero masters Go, Chess and Shogi](https://arxiv.org/abs/1712.01815)\n- 2018 Jan: [Soft Actor-Critic](https://ai.googleblog.com/2019/01/soft-actor-critic-deep-reinforcement.html)\n- 2018 Feb: [IMPALA](https://deepmind.com/blog/article/impala-scalable-distributed-deeprl-dmlab-30)\n- 2018 Jun: [Qt-Opt](https://ai.googleblog.com/2018/06/scalable-deep-reinforcement-learning.html)\n- 2018 Nov: [Go-Explore solved Montezuma’s Revenge](https://eng.uber.com/go-explore/)\n- 2018 Dec: [AlphaZero becomes the strongest player in history for chess, Go, and Shogi](https://deepmind.com/blog/article/alphazero-shedding-new-light-grand-games-chess-shogi-and-go)\n- 2019 Apr: [OpenAI Five defeated world champions at DotA 2](https://openai.com/five/)\n- 2019 May: [FTW Quake III Arena Capture the Flag](https://deepmind.com/blog/article/capture-the-flag-science)\n- 2019 Aug: [AlphaStar: Grandmaster level in StarCraft II](https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning)\n- 2019 Sep: [Emergent Tool Use from Multi-Agent Interaction](https://openai.com/blog/emergent-tool-use/)\n- 2019 Oct: [Solving Rubik’s Cube with a Robot Hand](https://openai.com/blog/solving-rubiks-cube/)\n- 2020 Mar: [Agent57 outperforms the standard human benchmark on all 57 Atari games](https://deepmind.com/blog/article/Agent57-Outperforming-the-human-Atari-benchmark)\n- 2020 Nov: [AlphaFold for protein folding](https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology)\n- 2020 Dec: [MuZero masters Go, chess, shogi and Atari without rules](https://deepmind.com/blog/article/muzero-mastering-go-chess-shogi-and-atari-without-rules)\n- 2021 Aug: [Generally capable agents emerge from open-ended play](https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play)\n\n## Books\n\n- [Algorithms for Reinforcement Learning. *Szepesvari et. al.*](https://www.amazon.com/Algorithms-Reinforcement-Learning-Csaba-Szepesvari/dp/1608454924)\n- [An Introduction to Deep Reinforcement Learning. *Francois-Lavet et. al.*](https://www.amazon.com/dp/1680835386)\n- [Deep Reinforcement Learning Hands-On. *Lapan*](https://www.amazon.com/Deep-Reinforcement-Learning-Hands-optimisation/dp/1838826998)\n- [Deep Reinforcement Learning in Action. *Zai \u0026 Brown*](https://www.amazon.com/Deep-Reinforcement-Learning-Action-Alexander/dp/1617295434)\n- [Foundations of Deep Reinforcement Learning. *Graesser \u0026 Keng*](https://www.amazon.com/dp/0135172381)\n- [Grokking Deep Reinforcement Learning. *Morales*](https://www.amazon.com/Grokking-Reinforcement-Learning-Miguel-Morales/dp/1617295450)\n- [Reinforcement Learning: An Introduction. *Sutton \u0026 Barto.*](https://www.amazon.com/dp/0262039249)\n\n## Tutorials\n\n- [Andrew Karpathy Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/)\n- [Arthur Juliani Simple Reinforcement Learning in Tensorflow Series](https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0)\n- [Berkeley Deep Reinforcement Learning Course](http://rail.eecs.berkeley.edu/deeprlcourse/)\n- [David Silver UCL Course on RL 2015](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)\n- [Deep RL Bootcamp 2017](https://sites.google.com/view/deep-rl-bootcamp/lectures)\n- [DeepMind UCL Deep RL Course 2018](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs)\n- [DeepMind Learning Resources](https://deepmind.com/learning-resources)\n- [dennybritz/reinforcement-learning](https://github.com/dennybritz/reinforcement-learning)\n- [higgsfield/RL-Adventure-2](https://github.com/higgsfield/RL-Adventure-2)\n- [higgsfield/RL-Adventure](https://github.com/higgsfield/RL-Adventure)\n- [The Hugging Face Deep Reinforcement Learning Class 🤗](https://github.com/huggingface/deep-rl-class#the-hugging-face-deep-reinforcement-learning-class-)\n- [MorvanZhou/Reinforcement Learning Methods and Tutorials](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow)\n- [OpenAI Spinning Up](https://github.com/openai/spinningup)\n- [Sergey Levine CS294 Deep Reinforcement Learning Fall 2017](http://rail.eecs.berkeley.edu/deeprlcourse-fa17/index.html)\n- [Udacity Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893)\n- [Reinforcement Learning Fundamental](https://www.youtube.com/playlist?list=PLzvYlJMoZ02Dxtwe-MmH4nOB5jYlMGBjr)\n- [PPOxFamily: DRL Tutorial Course](https://github.com/opendilab/PPOxFamily)\n\n## Blogs\n\n- [Alex Irpan](https://www.alexirpan.com)\n- [Andrew Karpathy](http://karpathy.github.io/)\n- [Berkeley AI Research](https://bair.berkeley.edu/blog/)\n- [Chris Olah](https://colah.github.io/)\n- [David Ha](http://blog.otoro.net/)\n- [DeepMind](https://deepmind.com/blog)\n- [Distill](https://distill.pub)\n- [Eric Jang](https://blog.evjang.com)\n- [Facebook AI](https://ai.facebook.com/blog/)\n- [Google AI](https://ai.googleblog.com/)\n- [Lilian Weng](https://lilianweng.github.io/lil-log/)\n- [Matthew Rahtz](http://amid.fish/)\n- [OpenAI](https://openai.com/blog/)\n- [The Gradient](https://thegradient.pub/)\n- [Uber AI](https://eng.uber.com/category/articles/ai/)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/kengz%2Fawesome-deep-rl/projects"}