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https://github.com/kengz/awesome-deep-rl

A curated list of awesome Deep Reinforcement Learning resources.
https://github.com/kengz/awesome-deep-rl

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A curated list of awesome Deep Reinforcement Learning resources.

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# Awesome Deep RL [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

A curated list of awesome Deep Reinforcement Learning resources.



## Contents



- [Libraries](#libraries)

- [Benchmark Results](#benchmark-results)

- [Environments](#environments)

- [Competitions](#competitions)

- [Timeline](#timeline)

- [Books](#books)

- [Tutorials](#tutorials)

- [Blog](#blogs)



## Libraries



- [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.

- [Berkeley Softlearning](https://github.com/rail-berkeley/softlearning) - A reinforcement learning framework for training maximum entropy policies in continuous domains.

- [Catalyst](https://github.com/catalyst-team/catalyst) - Accelerated DL & RL.

- [ChainerRL](https://github.com/chainer/chainerrl) - A deep reinforcement learning library built on top of Chainer.

- [DeepMind Acme](https://github.com/deepmind/acme) - A research framework for reinforcement learning.

- [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.

- [DeepMind TRFL](https://github.com/deepmind/trfl) - TensorFlow Reinforcement Learning.

- [DeepRL](https://github.com/ShangtongZhang/DeepRL) - Modularized Implementation of Deep RL Algorithms in PyTorch.

- [DeepX machina](https://github.com/DeepX-inc/machina) - A library for real-world Deep Reinforcement Learning which is built on top of PyTorch.

- [Facebook ELF](https://github.com/pytorch/ELF) - A platform for game research with AlphaGoZero/AlphaZero reimplementation.

- [Facebook ReAgent](https://github.com/facebookresearch/ReAgent) - A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)

- [garage](https://github.com/rlworkgroup/garage) - A toolkit for reproducible reinforcement learning research.

- [Google Dopamine](https://github.com/google/dopamine) - A research framework for fast prototyping of reinforcement learning algorithms.

- [Google TF-Agents](https://github.com/tensorflow/agents) - TF-Agents is a library for Reinforcement Learning in TensorFlow.

- [MAgent](https://github.com/geek-ai/MAgent) - A Platform for Many-agent Reinforcement Learning.

- [Maze](https://github.com/enlite-ai/maze) - Application-oriented deep reinforcement learning framework addressing real-world decision problems.

- [MushroomRL](https://github.com/MushroomRL/mushroom-rl) - Python library for Reinforcement Learning experiments.

- [NervanaSystems coach](https://github.com/NervanaSystems/coach) - Reinforcement Learning Coach by Intel AI Lab.

- [OpenAI Baselines](https://github.com/openai/baselines) - High-quality implementations of reinforcement learning algorithms.

- [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).

- [pytorch-rl](https://github.com/navneet-nmk/pytorch-rl) - Model-free deep reinforcement learning algorithms implemented in Pytorch.

- [reaver](https://github.com/inoryy/reaver) - A modular deep reinforcement learning framework with a focus on various StarCraft II based tasks.

- [RLgraph](https://github.com/rlgraph/rlgraph) - Modular computation graphs for deep reinforcement learning.

- [RLkit](https://github.com/vitchyr/rlkit) - Reinforcement learning framework and algorithms implemented in PyTorch.

- [rlpyt](https://github.com/astooke/rlpyt) - Reinforcement Learning in PyTorch.

- [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).

- [SLM Lab](https://github.com/kengz/SLM-Lab) - Modular Deep Reinforcement Learning framework in PyTorch.

- [Stable Baselines](https://github.com/hill-a/stable-baselines) - A fork of OpenAI Baselines, implementations of reinforcement learning algorithms.

- [TensorForce](https://github.com/tensorforce/tensorforce) - A TensorFlow library for applied reinforcement learning.

- [Tianshou](https://github.com/thu-ml/tianshou/) - Tianshou (天授) is a reinforcement learning platform based on pure PyTorch.

- [UMass Amherst Autonomous Learning Library](https://github.com/cpnota/autonomous-learning-library) - A PyTorch library for building deep reinforcement learning agents.

- [Unity ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents) - Unity Machine Learning Agents Toolkit.

- [vel](https://github.com/MillionIntegrals/vel) - Bring velocity to deep-learning research.

- [DI-engine](https://github.com/opendilab/DI-engine) - A generalized decision intelligence engine. It supports various Deep RL algorithms.



## Benchmark Results



- [DeepMind bsuite](https://github.com/deepmind/bsuite/tree/master/bsuite)

- [OpenAI baselines-results](https://github.com/openai/baselines-results)

- [OpenAI Baselines](https://github.com/openai/baselines#benchmarks)

- [OpenAI Spinning Up](https://spinningup.openai.com/en/latest/spinningup/bench.html)

- [ray rl-experiments](https://github.com/ray-project/rl-experiments)

- [rl-baselines-zoo](https://github.com/araffin/rl-baselines-zoo/blob/master/benchmark.md)

- [SLM Lab](https://github.com/kengz/SLM-Lab/blob/master/BENCHMARK.md)

- [vel](https://blog.millionintegrals.com/vel-pytorch-meets-baselines)

- [What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study](https://arxiv.org/abs/2006.05990)

- [yarlp](https://github.com/btaba/yarlp)



## Environments



- [AI2-THOR](https://github.com/allenai/ai2thor) - A near photo-realistic interactable framework for AI agents.

- [Animal-AI Olympics](https://github.com/beyretb/AnimalAI-Olympics) - An AI competition with tests inspired by animal cognition.

- [Berkeley rl-generalization](https://github.com/sunblaze-ucb/rl-generalization) - Modifiable OpenAI Gym environments for studying generalization in RL.

- [BTGym](https://github.com/Kismuz/btgym) - Scalable event-driven RL-friendly backtesting library. Build on top of Backtrader with OpenAI Gym environment API.

- [Carla](https://github.com/carla-simulator/carla) - Open-source simulator for autonomous driving research.

- [CuLE](https://github.com/NVlabs/cule) - A CUDA port of the Atari Learning Environment (ALE).

- [Deepdrive](https://github.com/deepdrive/deepdrive) - End-to-end simulation for self-driving cars.

- [DeepMind AndroidEnv](https://github.com/deepmind/android_env) - A library for doing RL research on Android devices.

- [DeepMind DM Control](https://github.com/deepmind/dm_control) - The DeepMind Control Suite and Package.

- [DeepMind Lab](https://github.com/deepmind/lab) - A customisable 3D platform for agent-based AI research.

- [DeepMind pycolab](https://github.com/deepmind/pycolab) - A highly-customisable gridworld game engine with some batteries included.

- [DeepMind PySC2](https://github.com/deepmind/pysc2) - StarCraft II Learning Environment.

- [DeepMind RL Unplugged](https://github.com/deepmind/deepmind-research/tree/master/rl_unplugged) - Benchmarks for Offline Reinforcement Learning.

- [Facebook EmbodiedQA](https://github.com/facebookresearch/EmbodiedQA) - Train embodied agents that can answer questions in environments.

- [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.

- [Facebook House3D](https://github.com/facebookresearch/House3D) - A Rich and Realistic 3D Environment.

- [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.

- [Google Research Football](https://github.com/google-research/football) - An RL environment based on open-source game Gameplay Football.

- [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.

- [gym-doom](https://github.com/ppaquette/gym-doom) - Doom environments based on VizDoom.

- [gym-duckietown](https://github.com/duckietown/gym-duckietown) - Self-driving car simulator for the Duckietown universe.

- [gym-gazebo2](https://github.com/AcutronicRobotics/gym-gazebo2) - A toolkit for developing and comparing reinforcement learning algorithms using ROS 2 and Gazebo.

- [gym-ignition](https://github.com/robotology/gym-ignition) - Experimental OpenAI Gym environments implemented with Ignition Robotics.

- [gym-idsgame](https://github.com/Limmen/gym-idsgame) - An Abstract Cyber Security Simulation and Markov Game for OpenAI Gym

- [gym-super-mario](https://github.com/ppaquette/gym-super-mario) - 32 levels of original Super Mario Bros.

- [Holodeck](https://github.com/BYU-PCCL/holodeck) - High Fidelity Simulator for Reinforcement Learning and Robotics Research.

- [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

- [ma-gym](https://github.com/koulanurag/ma-gym) - A collection of multi agent environments based on OpenAI gym.

- [mazelab](https://github.com/zuoxingdong/mazelab) - A customizable framework to create maze and gridworld environments.

- [Meta-World](https://github.com/rlworkgroup/metaworld) - An open source robotics benchmark for meta- and multi-task reinforcement learning.

- [Microsoft AirSim](https://github.com/Microsoft/AirSim) - Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research.

- [Microsoft Jericho](https://github.com/microsoft/jericho) - A learning environment for man-made Interactive Fiction games.

- [Microsoft Malmö](https://github.com/Microsoft/malmo) - A platform for Artificial Intelligence experimentation and research built on top of Minecraft.

- [Microsoft MazeExplorer](https://github.com/microsoft/MazeExplorer) - Customisable 3D environment for assessing generalisation in Reinforcement Learning.

- [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.

- [MineRL](https://github.com/minerllabs/minerl) - MineRL Competition for Sample Efficient Reinforcement Learning.

- [MuJoCo](http://www.mujoco.org) - Advanced physics simulation.

- [OpenAI Coinrun](https://github.com/openai/coinrun) - Code for the environments used in the paper Quantifying Generalization in Reinforcement Learning.

- [OpenAI Gym Retro](https://github.com/openai/retro) - Retro Games in Gym.

- [OpenAI Gym Soccer](https://github.com/openai/gym-soccer) - A multiagent domain featuring continuous state and action spaces.

- [OpenAI Gym](https://github.com/openai/gym) - A toolkit for developing and comparing reinforcement learning algorithms.

- [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.

- [OpenAI Neural MMO](https://github.com/openai/neural-mmo) - A Massively Multiagent Game Environment.

- [OpenAI Procgen Benchmark](https://github.com/openai/procgen) - Procedurally Generated Game-Like Gym Environments.

- [OpenAI Roboschool](https://github.com/openai/roboschool) - Open-source software for robot simulation, integrated with OpenAI Gym.

- [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.

- [OpenAI Safety Gym](https://github.com/openai/safety-gym) - Tools for accelerating safe exploration research.

- [Personae](https://github.com/Ceruleanacg/Personae) - RL & SL Methods and Envs For Quantitative Trading.

- [Pommerman](https://github.com/MultiAgentLearning/playground) - A clone of Bomberman built for AI research.

- [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

- [PyGame Learning Environment](https://github.com/ntasfi/PyGame-Learning-Environment) - Reinforcement Learning Environment in Python.

- [RLBench](https://github.com/stepjam/RLBench) - A large-scale benchmark and learning environment.

- [RLGym](https://github.com/lucas-emery/rocket-league-gym) - A python API to treat the game Rocket League as an OpenAI Gym environment.

- [RLTrader](https://github.com/notadamking/RLTrader) - A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym.

- [RoboNet](https://blog.ml.cmu.edu/2019/11/26/robonet/) - A Dataset for Large-Scale Multi-Robot Learning.

- [rocket-lander](https://github.com/arex18/rocket-lander) - SpaceX Falcon 9 Box2D continuous-action simulation with traditional and AI controllers.

- [Stanford Gibson Environments](https://github.com/StanfordVL/GibsonEnv) - Real-World Perception for Embodied Agents.

- [Stanford osim-rl](https://github.com/stanfordnmbl/osim-rl) - Reinforcement learning environments with musculoskeletal models.

- [Unity ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents) - Unity Machine Learning Agents Toolkit.

- [UnityObstableTower](https://github.com/Unity-Technologies/obstacle-tower-env) - A procedurally generated environment consisting of multiple floors to be solved by a learning agent.

- [VizDoom](https://github.com/mwydmuch/ViZDoom) - Doom-based AI Research Platform for Reinforcement Learning from Raw Visual Information.

- [RLCard](https://github.com/datamllab/rlcard/) - A research platform for reinforcement learning in card games.

- [DouZero](https://github.com/kwai/DouZero/) - A research platform for reinforcement learning in DouDizhu (Chinese poker).



## Competitions



- [AWS DeepRacer League 2019](https://aws.amazon.com/deepracer/league/)

- [Flatland Challenge 2019](https://www.aicrowd.com/challenges/flatland-challenge)

- [Kaggle Connect X Competition 2020](https://www.kaggle.com/c/connectx)

- [NeurIPS 2019: Animal-AI Olympics](http://animalaiolympics.com/)

- [NeurIPS 2019: Game of Drones](https://www.microsoft.com/en-us/research/academic-program/game-of-drones-competition-at-neurips-2019/)

- [NeurIPS 2019: Learn to Move - Walk Around](https://www.aicrowd.com/challenges/neurips-2019-learning-to-move-walk-around)

- [NeurIPS 2019: MineRL Competition](http://minerl.io/competition/)

- [NeurIPS 2019: Reconnaissance Blind Chess](https://rbc.jhuapl.edu/)

- [NeurIPS 2019: Robot open-Ended Autonomous Learning](https://www.aicrowd.com/challenges/robot-open-ended-autonomous-learning-real)

- [Unity Obstacle Tower Challenge 2019](https://blogs.unity3d.com/2019/01/28/obstacle-tower-challenge-test-the-limits-of-intelligence-systems/)



>Check [AICrowd](https://www.aicrowd.com) for the latest list of major RL competitions



## Timeline



- 1947: [Monte Carlo Sampling](http://eniacinaction.com/the-articles/3-los-alamos-bets-on-eniac-nuclear-monte-carlo-simulations-1947-8/)

- 1958: [Perceptron](https://www.ling.upenn.edu/courses/cogs501/Rosenblatt1958.pdf)

- 1959: [Temporal Difference Learning](https://dl.acm.org/citation.cfm?id=1661924)

- 1983: [ASE-ALE — the first Actor-Critic algorithm](https://psycnet.apa.org/record/1984-13799-001)

- 1986: [Backpropagation algorithm](https://www.nature.com/articles/323533a0)

- 1989: [CNNs](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.479&rep=rep1&type=pdf)

- 1989: [Q-Learning](http://www.cs.rhul.ac.uk/~chrisw/new_thesis.pdf)

- 1991: [TD-Gammon](http://bkgm.com/books/Robertie-LearningFromTheMachine.html)

- 1992: [REINFORCE](https://dl.acm.org/citation.cfm?id=139614)

- 1992: [Experience Replay](https://dl.acm.org/citation.cfm?id=139620)

- 1994: [SARSA](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.17.2539)

- 1999: [Nvidia invented the GPU](https://www.nvidia.com/object/gpu.html)

- 2007: [CUDA released](https://developer.nvidia.com/cuda-zone)

- 2012: [Arcade Learning Environment (ALE)](https://arxiv.org/abs/1207.4708)

- 2013: [DQN](https://arxiv.org/abs/1312.5602)

- 2015 Feb: [DQN human-level control in Atari](https://www.nature.com/articles/nature14236)

- 2015 Feb: [TRPO](https://arxiv.org/abs/1502.05477)

- 2015 Jun: [Generalized Advantage Estimation](https://arxiv.org/abs/1506.02438)

- 2015 Sep: [Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/abs/1509.02971)

- 2015 Sep: [DoubleDQN](https://arxiv.org/abs/1509.06461)

- 2015 Nov: [DuelingDQN](https://arxiv.org/abs/1511.06581)

- 2015 Nov: [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952)

- 2015 Nov: [TensorFlow](https://www.tensorflow.org/)

- 2016 Feb: [A3C](https://arxiv.org/abs/1602.01783)

- 2016 Mar: [AlphaGo beats Lee Sedol 4-1](https://deepmind.com/alphago-korea)

- 2016 Jun: [OpenAI Gym](https://github.com/openai/gym)

- 2016 Jun: [Generative Adversarial Imitation Learning (GAIL)](https://arxiv.org/abs/1606.03476)

- 2016 Oct: [PyTorch](https://pytorch.org/)

- 2017 Mar: [Model-Agnostic Meta-Learning (MAML)](https://arxiv.org/abs/1703.03400)

- 2017 Jul: [Distributional RL](https://arxiv.org/abs/1707.06887)

- 2017 Jul: [PPO](https://arxiv.org/abs/1707.06347)

- 2017 Aug: [OpenAI DotA 2 1:1](https://openai.com/blog/more-on-dota-2/)

- 2017 Aug: [Intrinsic Cusiority Module (ICM)](https://arxiv.org/abs/1705.05363)

- 2017 Oct: [Rainbow](https://arxiv.org/abs/1710.02298)

- 2017 Oct: [AlphaGo Zero masters Go without human knowledge](https://deepmind.com/blog/article/alphago-zero-starting-scratch)

- 2017 Dec: [AlphaZero masters Go, Chess and Shogi](https://arxiv.org/abs/1712.01815)

- 2018 Jan: [Soft Actor-Critic](https://ai.googleblog.com/2019/01/soft-actor-critic-deep-reinforcement.html)

- 2018 Feb: [IMPALA](https://deepmind.com/blog/article/impala-scalable-distributed-deeprl-dmlab-30)

- 2018 Jun: [Qt-Opt](https://ai.googleblog.com/2018/06/scalable-deep-reinforcement-learning.html)

- 2018 Nov: [Go-Explore solved Montezuma’s Revenge](https://eng.uber.com/go-explore/)

- 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)

- 2019 Apr: [OpenAI Five defeated world champions at DotA 2](https://openai.com/five/)

- 2019 May: [FTW Quake III Arena Capture the Flag](https://deepmind.com/blog/article/capture-the-flag-science)

- 2019 Aug: [AlphaStar: Grandmaster level in StarCraft II](https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning)

- 2019 Sep: [Emergent Tool Use from Multi-Agent Interaction](https://openai.com/blog/emergent-tool-use/)

- 2019 Oct: [Solving Rubik’s Cube with a Robot Hand](https://openai.com/blog/solving-rubiks-cube/)

- 2020 Mar: [Agent57 outperforms the standard human benchmark on all 57 Atari games](https://deepmind.com/blog/article/Agent57-Outperforming-the-human-Atari-benchmark)

- 2020 Nov: [AlphaFold for protein folding](https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology)

- 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)

- 2021 Aug: [Generally capable agents emerge from open-ended play](https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play)



## Books



- [Algorithms for Reinforcement Learning. *Szepesvari et. al.*](https://www.amazon.com/Algorithms-Reinforcement-Learning-Csaba-Szepesvari/dp/1608454924)

- [An Introduction to Deep Reinforcement Learning. *Francois-Lavet et. al.*](https://www.amazon.com/dp/1680835386)

- [Deep Reinforcement Learning Hands-On. *Lapan*](https://www.amazon.com/Deep-Reinforcement-Learning-Hands-optimisation/dp/1838826998)

- [Deep Reinforcement Learning in Action. *Zai & Brown*](https://www.amazon.com/Deep-Reinforcement-Learning-Action-Alexander/dp/1617295434)

- [Foundations of Deep Reinforcement Learning. *Graesser & Keng*](https://www.amazon.com/dp/0135172381)

- [Grokking Deep Reinforcement Learning. *Morales*](https://www.amazon.com/Grokking-Reinforcement-Learning-Miguel-Morales/dp/1617295450)

- [Reinforcement Learning: An Introduction. *Sutton & Barto.*](https://www.amazon.com/dp/0262039249)



## Tutorials



- [Andrew Karpathy Deep Reinforcement Learning: Pong from Pixels](http://karpathy.github.io/2016/05/31/rl/)

- [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)

- [Berkeley Deep Reinforcement Learning Course](http://rail.eecs.berkeley.edu/deeprlcourse/)

- [David Silver UCL Course on RL 2015](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)

- [Deep RL Bootcamp 2017](https://sites.google.com/view/deep-rl-bootcamp/lectures)

- [DeepMind UCL Deep RL Course 2018](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs)

- [DeepMind Learning Resources](https://deepmind.com/learning-resources)

- [dennybritz/reinforcement-learning](https://github.com/dennybritz/reinforcement-learning)

- [higgsfield/RL-Adventure-2](https://github.com/higgsfield/RL-Adventure-2)

- [higgsfield/RL-Adventure](https://github.com/higgsfield/RL-Adventure)

- [The Hugging Face Deep Reinforcement Learning Class 🤗](https://github.com/huggingface/deep-rl-class#the-hugging-face-deep-reinforcement-learning-class-)

- [MorvanZhou/Reinforcement Learning Methods and Tutorials](https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow)

- [OpenAI Spinning Up](https://github.com/openai/spinningup)

- [Sergey Levine CS294 Deep Reinforcement Learning Fall 2017](http://rail.eecs.berkeley.edu/deeprlcourse-fa17/index.html)

- [Udacity Deep Reinforcement Learning Nanodegree](https://www.udacity.com/course/deep-reinforcement-learning-nanodegree--nd893)

- [Reinforcement Learning Fundamental](https://www.youtube.com/playlist?list=PLzvYlJMoZ02Dxtwe-MmH4nOB5jYlMGBjr)

- [PPOxFamily: DRL Tutorial Course](https://github.com/opendilab/PPOxFamily)



## Blogs



- [Alex Irpan](https://www.alexirpan.com)

- [Andrew Karpathy](http://karpathy.github.io/)

- [Berkeley AI Research](https://bair.berkeley.edu/blog/)

- [Chris Olah](https://colah.github.io/)

- [David Ha](http://blog.otoro.net/)

- [DeepMind](https://deepmind.com/blog)

- [Distill](https://distill.pub)

- [Eric Jang](https://blog.evjang.com)

- [Facebook AI](https://ai.facebook.com/blog/)

- [Google AI](https://ai.googleblog.com/)

- [Lilian Weng](https://lilianweng.github.io/lil-log/)

- [Matthew Rahtz](http://amid.fish/)

- [OpenAI](https://openai.com/blog/)

- [The Gradient](https://thegradient.pub/)

- [Uber AI](https://eng.uber.com/category/articles/ai/)