{"id":1152,"url":"https://github.com/dbobrenko/awesome-rl","name":"awesome-rl","description":"Awesome RL: Papers, Books, Codes, Benchmarks","projects_count":73,"last_synced_at":"2026-10-03T18:00:25.390Z","repository":{"id":37525785,"uuid":"78960411","full_name":"dbobrenko/awesome-rl","owner":"dbobrenko","description":"Awesome RL: Papers, Books, Codes, Benchmarks","archived":false,"fork":false,"pushed_at":"2023-10-29T14:32:15.000Z","size":280,"stargazers_count":119,"open_issues_count":0,"forks_count":17,"subscribers_count":10,"default_branch":"master","last_synced_at":"2026-09-13T22:17:18.575Z","etag":null,"topics":["arxiv","awesome-deep-rl","awesome-list","awesome-rl","deep-learning","deep-reinforcement-learning","machine-learning","notes","paper-notes","reinforcement-learning"],"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/dbobrenko.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}},"created_at":"2017-01-14T19:04:51.000Z","updated_at":"2026-08-20T05:59:21.000Z","dependencies_parsed_at":"2024-01-05T21:43:48.981Z","dependency_job_id":"cbc3896b-bc2f-4fd2-b90b-53fae36d40b4","html_url":"https://github.com/dbobrenko/awesome-rl","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dbobrenko/awesome-rl","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbobrenko%2Fawesome-rl","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbobrenko%2Fawesome-rl/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbobrenko%2Fawesome-rl/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbobrenko%2Fawesome-rl/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dbobrenko","download_url":"https://codeload.github.com/dbobrenko/awesome-rl/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbobrenko%2Fawesome-rl/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":343072799,"owners_count":37978412,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-08-22T15:14:58.755Z","status":"online","status_checked_at":"2026-10-03T02:00:07.567Z","response_time":59,"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":"2024-01-04T17:46:58.620Z","updated_at":"2026-10-03T18:00:25.391Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["RL Frameworks \u0026 Implementations \u003ca name=\"frameworks\"\u003e\u003c/a\u003e","![auto-ml] Auto ML \u003ca name=\"auto-ml\"\u003e\u003c/a\u003e","RL Benchmarks \u003ca name=\"benchmarks\"\u003e\u003c/a\u003e","Policy-Based Generic Agents \u003ca name=\"policy-agents\"\u003e\u003c/a\u003e","Value-Based Generic Agents \u003ca name=\"value-agents\"\u003e\u003c/a\u003e","![model-based] Model-Based Generic Agents  \u003ca name=\"model-based\"\u003e\u003c/a\u003e","![evolution] Evolutionary Algorithms \u003ca name=\"evolution\"\u003e\u003c/a\u003e","![exploration] Exploration \u003ca name=\"exploration\"\u003e\u003c/a\u003e","![self-play] Self-Play \u003ca name=\"self-play\"\u003e\u003c/a\u003e","![meta-learning] Meta-Learning","![multi-agent-rl] Multi-Agent RL","![inverse-rl] Inverse RL \u003ca name=\"inverse-rl\"\u003e\u003c/a\u003e","![navigation] Navigation \u003ca name=\"navigation\"\u003e\u003c/a\u003e","![manipulation] Manipulation \u003ca name=\"manipulation\"\u003e\u003c/a\u003e","![locomotion] Locomotion \u003ca name=\"locomotion\"\u003e\u003c/a\u003e","Other Domains  \u003ca name=\"other-domains\"\u003e\u003c/a\u003e","Search for new Papers","Misc","Books"],"sub_categories":[],"readme":"# Awesome Reinforcement Learning\n\n\u003cdetails\u003e\n  \u003csummary\u003e\u003cb\u003e\u003ci\u003eClick here to see icon descriptions.\u003c/i\u003e\u003c/b\u003e\u003c/summary\u003e\n  \n- :rocket: - state-of-the-art agent/technique at the moment of paper publication.\n- :star: - valuable paper.\n- ![model-based] - Model-based RL.\n- ![multi-agent-rl] - Multi-Agent RL.\n- ![self-play] - Self-Play.\n- ![evolution] - Evolutionary \u0026 Genetic Algorithms.\n- ![generalization] - Generalization on unseen environments.\n- ![auto-ml] - Auto ML - Architecture search.\n- ![manipulation] - Manipulation tasks.\n- ![locomotion] - Locomotion: MuJoCo, Roboschool, etc.\n- ![navigation] - Navigation tasks.\n- ![plan] - Strategy Planning Problems.\n- ![transfer] - Transfer learning.\n- ![inverse-rl] - Inverse Reinforcement Learning.\n- ![meta-learning] - Meta-Learning.\n- ![exploration] - Curiosity Learning, Advanced Exploration.\n- ![table] - Table games (Table).\n- ![atari] - Atari game (Atari).\n- ![doom] - Doom game (Doom).\n- ![sc] - Starcraft game (Starcraft).\n- ![go] - Go game (Go).\n\n\u003c/details\u003e\n\n## Table of Contents\n  - [Frameworks](#frameworks)\n  - [Benchmarks](#benchmarks)\n  - [Policy-Based Generic Agents](#policy-agents)\n  - [Value-Based Generic Agents](#value-agents)\n  - [![model-based] Model-Based Generic Agents](#model-based)\n  - [![evolution] Evolutionary \u0026 Genetic Algorithms](#evolution)\n  - [![exploration] Exploration](#exploration)\n  - [![self-play] Self-Play](#self-play)\n  - [![meta-learning] Meta-Learning](#meta-learning)\n  - [![multi-agent-rl] Multi-Agent RL](#multi-agent-rl)\n  - [![inverse-rl] Inverse RL](#inverse-rl)\n  - [![navigation] Navigation](#navigation)\n  - [![manipulation] Manipulation](#manipulation)\n  - [![locomotion] Locomotion](#locomotion)\n  - [![auto-ml] Auto ML](#auto-ml)\n  - [Other Domains](#other-domains)\n  - [Books](#books)\n  - [Search for new Papers](#search-for-new-papers)\n  - [Misc](#misc)\n\n\n## RL Frameworks \u0026 Implementations \u003ca name=\"frameworks\"\u003e\u003c/a\u003e\n[[Stable Baselines3](https://stable-baselines3.readthedocs.io/en/master/)] PyTorch: MaskablePPO, PPO, A2C, DQN, etc\n\n[[Baselines @ OpenAI](https://github.com/openai/baselines)] TensorFlow: PPO, A2C, DQN, TRPO, ACKTR, DDPG, HER, GAIL, etc\n\n[[Baselines @ DLR-RM](https://github.com/DLR-RM/stable-baselines3)] Pytorch: Custom envs, custom policies\n  \n[[RLlib @ Ray](https://github.com/ray-project/ray/tree/master/rllib) Pytorch / TensorFlow]\n  \n[[Dopamine @ Google](https://github.com/google/dopamine)] TensorFlow: Rainbow, c51, IQN, DQN, etc\n \n[[TensorForce](https://github.com/reinforceio/tensorforce)] TensorFlow: A3C, PPO, TRPO, DQN, etc\n\n[[pytorch-a2c-ppo-acktr](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail)] PyTorch: A2C, ACKTR, PPO, GAIL, etc\n\n## RL Benchmarks \u003ca name=\"benchmarks\"\u003e\u003c/a\u003e\n[[OpenAI Benchmarks for PPO, A2C, ACKTR, ACER](https://github.com/openai/baselines-results/blob/master/acktr_ppo_acer_a2c_atari.ipynb)]\n \n[[OpenAI Benchmarks for DQN, Double DQN, Dueling DQN, Prioritized DQN](https://github.com/openai/baselines-results/blob/master/dqn_results.ipynb)]\n\n[[Google Benchmarks for Rainbow, c51, IQN, DQN](https://google.github.io/dopamine/baselines/plots.html)]\n\n## Policy-Based Generic Agents \u003ca name=\"policy-agents\"\u003e\u003c/a\u003e\n:rocket: [[Soft Actor Critic](https://arxiv.org/abs/1812.05905)] [[blog](https://ai.googleblog.com/2019/01/soft-actor-critic-deep-reinforcement.html)] [[code](https://github.com/rail-berkeley/softlearning/)] 2018 @ Google Brain, UC Berkeley\n\n:rocket: [[IMPALA](https://arxiv.org/abs/1802.01561v2)] 2018 @ Uber AI Labs\n  \n:rocket: [[Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR, A2C)](https://arxiv.org/abs/1708.05144)] 2018; Univ. of Toronto, New York Univ.\n\n:rocket: [[Proximal Policy Optimization Algorithms (PPO)](https://arxiv.org/abs/1707.06347)] [[blog](https://blog.openai.com/openai-baselines-ppo/)] 2017 @ OpenAI\n\n:rocket: [:pencil: Notes](./notes/a3c-agent.md) [[Asynchronous Methods for Deep Reinforcement Learning (A3C)](https://arxiv.org/abs/1602.01783v2)] 2016 @ Google Deepmind \n\n[[High-dimensional continuous control using generalized advantage estimation (GAE)](https://arxiv.org/abs/1506.02438)] 2015 @ Berkeley\n\n:star: [[Trust Region Policy Optimization (TRPO)](https://arxiv.org/abs/1502.05477)] 2015 @ UC Berkeley\n\n:star: [[Actor-Critic Algorithms, pdf](https://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf)] Konda and Tsitsiklis, 2003\n\n:star: [[Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning (REINFORCE), pdf](http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf)] Ronald J. Williams, 1992 @ Northeastern Univ.\n\n## Value-Based Generic Agents \u003ca name=\"value-agents\"\u003e\u003c/a\u003e\n:rocket: [[Implicit Quantile Networks for Distributional Reinforcement Learning (IQN)](https://arxiv.org/abs/1806.06923)] Dabney et al., 2018 @ Google Deepmind\n\n:rocket: [[A Distributional Perspective on Reinforcement Learning (c51)](https://arxiv.org/abs/1707.06887)] Bellemare et al., 2018 @ Google Deepmind\n\n:rocket: [[Rainbow: Combining Improvements in Deep Reinforcement Learning](https://arxiv.org/abs/1710.02298)] Hessel et al., 2017 @ Google Deepmind\n\n:rocket: [[Dueling Network Architectures for Deep Reinforcement Learning (Dueling DQN)](https://arxiv.org/abs/1511.06581)] Wang et al., 2015 @ Google Deepmind\n\n:rocket: [:pencil: Notes](./notes/prioritized-exp-replay.md) [[Prioritized Experience Replay](https://arxiv.org/abs/1511.05952v4)] Schaul et al., 2015 @ Google Deepmind\n\n:rocket: [[Deep Reinforcement Learning with Double Q-learning (Double DQN)](https://arxiv.org/abs/1509.06461)] Hasselt et al., 2015 @ Google Deepmind\n\n:rocket: [:pencil: Notes](./notes/dqn-agent.md) [[Human-level control through deep reinforcement learning (DQN)](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html)]\n[[pdf](hhttp://www.readcube.com/articles/10.1038/nature14236?shared_access_token=Lo_2hFdW4MuqEcF3CVBZm9RgN0jAjWel9jnR3ZoTv0P5kedCCNjz3FJ2FhQCgXkApOr3ZSsJAldp-tw3IWgTseRnLpAc9xQq-vTA2Z5Ji9lg16_WvCy4SaOgpK5XXA6ecqo8d8J7l4EJsdjwai53GqKt-7JuioG0r3iV67MQIro74l6IxvmcVNKBgOwiMGi8U0izJStLpmQp6Vmi_8Lw_A%3D%3D)]\nMnih et al., 2015 @ Google Deepmind\n\n:rocket: [[Playing Atari with Deep Reinforcement Learning** (DQN)](https://arxiv.org/abs/1312.5602)] Mnih et al., 2013 @ DeepMind Technologies\n\n:star: [[Temporal Difference Learning and TD-Gammon, pdf](http://cling.csd.uwo.ca/cs346a/extra/tdgammon.pdf)] Gerald Tesauro, 1995\n\n\n## ![model-based] Model-Based Generic Agents  \u003ca name=\"model-based\"\u003e\u003c/a\u003e\n[[Model-Based Reinforcement Learning for Atari](https://arxiv.org/abs/1903.00374)] 2019 @ Google Brain, etc\n\n:star: ![navigation] [[World Models](https://arxiv.org/abs/1803.10122)] [[blog](https://worldmodels.github.io/)] 2018 @ IDSIA, Google Brain, NNAISENSE\n\n![locomotion] [[Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning](https://arxiv.org/abs/1708.02596)] [[blog](http://bair.berkeley.edu/blog/2017/11/30/model-based-rl/)] [[code](https://github.com/nagaban2/nn_dynamics)] 2017 @ Berkeley\n\n![locomotion] [[Learning model-based planning from scratch](https://arxiv.org/abs/1707.06170)], [[blog](https://deepmind.com/blog/agents-imagine-and-plan/)] 2017 @ Google DeepMind\n\n![navigation] [[The Predictron: End-To-End Learning and Planning](https://arxiv.org/abs/1612.08810v2)] 2016 @ Google Deepmind\n\n\n## ![evolution] Evolutionary Algorithms \u003ca name=\"evolution\"\u003e\u003c/a\u003e\n[[Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari](https://arxiv.org/abs/1802.08842)] 2018 @ Univ. of Freiburg\n\n:star: ![locomotion] [[Deep Neuroevolution](https://arxiv.org/abs/1712.06567)] 2017 @ Uber AI Labs\n\n:star: [[Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://arxiv.org/abs/1703.03864v1)] 2017 @ OpenAI\n\n[[Evolving Large-Scale Neural Networks for Vision-Based Reinforcement Learning, pdf](http://people.idsia.ch/~juergen/gecco2013torcs.pdf)] 2013 @ IDSIA, USI-SUPSI\n\n\n## ![exploration] Exploration \u003ca name=\"exploration\"\u003e\u003c/a\u003e\n:rocket: [[Go-Explore](https://arxiv.org/abs/1901.10995)] 2019 @ Uber AI Labs\n\n[[Exploration by Random Network Distillation (RND)](https://arxiv.org/abs/1810.12894)] [[blog](https://blog.openai.com/reinforcement-learning-with-prediction-based-rewards/)] [[code](https://github.com/openai/random-network-distillation)] 2018 @ OpenAI\n\n![navigation] [[Large-Scale Study of Curiosity-Driven Learning](https://arxiv.org/abs/1808.04355)] [[blog](https://pathak22.github.io/large-scale-curiosity/)] 2018 @ OpenAI, Berkeley, Univ. of Edinburgh\n\n:star: [[RUDDER: Return Decomposition for Delayed Rewards](https://arxiv.org/abs/1806.07857)] [[code](https://github.com/ml-jku/baselines-rudder)] 2018 @ Johannes Kepler Univ. Linz\n\n[[Deep Curiosity Search](https://arxiv.org/abs/1806.00553)] 2018 @ Univ. of Wyoming\n\n![locomotion] [[Parameter Space Noise for Exploration](https://arxiv.org/abs/1706.01905)] 2017 @ OpenAI, Karlsruhe Inst. of Tech.\n\n:star: ![transfer] [[Imagination-Augmented Agents for Deep Reinforcement Learning (I2As)](https://arxiv.org/abs/1707.06203)] [[blog](https://deepmind.com/blog/agents-imagine-and-plan/)] 2017 @ DeepMind\n\n\n## ![self-play] Self-Play \u003ca name=\"self-play\"\u003e\u003c/a\u003e\n:star: ![table] [[Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://arxiv.org/abs/1712.01815)] Silver et al., 2017 @ Google Deepmind\n\n:star: ![table] [[Mastering the Game of Go without Human Knowledge (AlphaGo Zero), pdf](https://deepmind.com/documents/119/agz_unformatted_nature.pdf)], [[blog](https://deepmind.com/blog/alphago-zero-learning-scratch/)] Silver et al., 2017 @ Deepmind\n\n![table] [[Mastering the game of Go with deep neural networks and tree search (AlphaGo Master)](https://www.nature.com/nature/journal/v529/n7587/full/nature16961.html)], [[reddit](https://www.reddit.com/r/MachineLearning/comments/42ytdx/pdf_mastering_the_game_of_go_with_deep_neural/)] Silver et al., 2017 @ Deepmind, Google\n\n\n## ![meta-learning] Meta-Learning\n![locomotion] [[Meta Learning Shared Hierarchies](https://arxiv.org/abs/1710.09767)] [[blog](https://blog.openai.com/learning-a-hierarchy/)] Frans et al., 2017 @ OpenAI, Berkeley.\n\n[[Hybrid Reward Architecture for Reinforcement Learning (HRA)](https://arxiv.org/abs/1706.04208v1)] van Seijen et al., 2017 @ Microsoft Maluuba, McGill Univ.\n\n\n## ![multi-agent-rl] Multi-Agent RL\n[[Learning with Opponent-Learning Awareness (LOLA)](https://arxiv.org/abs/1709.04326)] [[blog](https://blog.openai.com/learning-to-model-other-minds/)] Foerster et al., 2017 @ OpenAI, Oxford, Berkeley, CMU\n\n\n## ![inverse-rl] Inverse RL \u003ca name=\"inverse-rl\"\u003e\u003c/a\u003e\n![manipulation] [[SFV: Reinforcement Learning of Physical Skills from Videos](https://arxiv.org/abs/1810.03599)] [[blog](https://bair.berkeley.edu/blog/2018/10/09/sfv/)] Peng et al., 2018; Berkeley\n\n![manipulation] [[One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning](https://arxiv.org/abs/1802.01557v1)] Finn et al., 2018 @ UC Berkeley\n  \n![manipulation] [[One-Shot Visual Imitation Learning via Meta-Learning](https://arxiv.org/abs/1709.04905)] Finn et al., 2017 @ UC Berkeley, OpenAI\n\n\n## ![navigation] Navigation \u003ca name=\"navigation\"\u003e\u003c/a\u003e\n[[Learning to Navigate in Cities Without a Map](https://arxiv.org/abs/1804.00168)] Mirowski et al, 2019 @ Deepmind\n\n[[Human-level performance in first-person multiplayer games with population-based deep reinforcement learning](https://arxiv.org/abs/1807.01281)] [[blog](https://deepmind.com/blog/capture-the-flag/)] Jaderberg et al, 2018 @ DeepMind\n\n![generalization] [[Building Generalizable Agents with a Realistic and Rich 3D Environment](https://arxiv.org/abs/1801.02209)] Wu et al, 2018 @ Berkeley, FAIR\n\n:rocket: [[Learning to Navigate in Complex Environments](https://arxiv.org/abs/1611.03673)] Mirowski et al., 2017 @ Deepmind\n\n![transfer] [Distral: Robust Multitask Reinforcement Learning](https://arxiv.org/abs/1707.04175)] Teh et al, 2017 @ Deepmind\n\n![meta-learning] [[RL\u003csup\u003e2\u003c/sup\u003e: Fast Reinforcement Learning via Slow Reinforcement Learning](https://arxiv.org/abs/1611.02779)] Duan et al., 2016 @ Berkeley, OpenAI\n\n:star: [:pencil: Notes](./notes/unreal-agent.md) ![locomotion] [[Reinforcement Learning with unsupervised auxiliary tasks (UNREAL)](https://arxiv.org/abs/1611.05397)] Jaderberg et al., 2016 @ Google DeepMind\n\n:rocket: [[Learning to act by predicting the future (VizDoom 2016 Full DM Winner)](https://arxiv.org/abs/1611.01779)] Dosovitskiy, Koltun, 2016 @ Intel Labs\n\n[[Playing FPS Games with Deep Reinforcement Learning (VizDoom 2016 Limited DM 2nd place)](https://arxiv.org/abs/1609.05521)] Lample, Chaplot, 2016 @ CMU\n\n\n## ![manipulation] Manipulation \u003ca name=\"manipulation\"\u003e\u003c/a\u003e\n![generalization] [[Learning Dexterous In-Hand Manipulation](https://arxiv.org/abs/1808.00177)] [[blog](https://blog.openai.com/learning-dexterity/)] Andrychowicz et al., 2018 @ OpenAI\n\n![generalization] [[Asymmetric Actor Critic for Image-Based Robot Learning](https://arxiv.org/abs/1710.06542)] [[blog](https://blog.openai.com/generalizing-from-simulation/)] Pinto et al., 2017 @ OpenAI, CMU\n\n![generalization] [[Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://arxiv.org/abs/1710.06537)], [[blog](https://blog.openai.com/generalizing-from-simulation/)] Peng et al., 2017 @ OpenAI, Berkeley\n\n## ![locomotion] Locomotion \u003ca name=\"locomotion\"\u003e\u003c/a\u003e\n[[Emergence of Locomotion Behaviours in Rich Environments](https://arxiv.org/abs/1707.02286)] [[blog](https://deepmind.com/blog/producing-flexible-behaviours-simulated-environments/)] Heess et al., 2017 @ DeepMind\n\n[[Programmable Agents](https://arxiv.org/abs/1706.06383v1)] Denil et al., 2017 @ Google Deepmind\n\n## ![auto-ml] Auto ML \u003ca name=\"auto-ml\"\u003e\u003c/a\u003e\n[[AutoAugment: Learning Augmentation Policies from Data](https://arxiv.org/abs/1805.09501)] Cubuk et al., 2018 @ Google Brain\n\n:star: ![evolution] [[Regularized Evolution for Image Classifier Architecture Search](https://arxiv.org/abs/1802.01548v2)] Real et al., 2018 @ Google Brain\n\n:star: [[Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012)] Zoph et al., 2017 @ Google Brain\n\n[[Neural Optimizer Search with Reinforcement Learning, pdf](http://proceedings.mlr.press/v70/bello17a/bello17a.pdf)] Bello et al., 2017 @ Google Brain\n\n[[Neural Architecture Search with Reinforcement Learning](https://arxiv.org/abs/1611.01578)] B. Zoph and Quoc V. Le, 2016 @ Google Brain\n\n\n## Other Domains  \u003ca name=\"other-domains\"\u003e\u003c/a\u003e\n[[A Deep Reinforcement Learning Chatbot](https://arxiv.org/abs/1709.02349)] Serban et al., 2017 @ MILA\n\n\n## Books\n:star: [[Reinforcement Learning: An Introduction, pdf](http://incompleteideas.net/book/bookdraft2018jan1.pdf)] Richard S. Sutton and Andrew G. Barto, 2018\n\n\n## Search for new Papers\n[[A Brief Survey of Deep Reinforcement Learning](https://arxiv.org/abs/1708.05866)] Arulkumaran et al., 2017\n\nAnother Awesome Deep RL list: https://github.com/tigerneil/awesome-deep-rl\n\nAwesome Offline RL: https://github.com/hanjuku-kaso/awesome-offline-rl\n\nArXiv Sanity Preserver: http://www.arxiv-sanity.com/\n  \nGitXiv: http://www.gitxiv.com/\n\n\n## Misc\n[[How to Read a Paper](http://ccr.sigcomm.org/online/files/p83-keshavA.pdf)] S. Keshav, 2007 @ Univ. of Waterloo\n\n[Transfromers: [Attention is all you need](https://arxiv.org/abs/1706.03762)] Vaswani et al. 2017 @ Google Brain/Research\n\n\n[atari]: ./icons/atari.png\t\"Atari 2600 games\"\n[doom]: ./icons/doom.png\t\"Doom game\"\n[sc]: ./icons/sc.png\t\"Starcraft game\"\n[go]: ./icons/go.png\t\"Go game\"\n[table]: ./icons/table.png\t\"Table games\"\n[auto-ml]: ./icons/nn.png\t\"Neural Networks \u0026 Optimizers\"\n[manipulation]: ./icons/robot.png\t\"Robotic hand manipuation\"\n[locomotion]: ./icons/loco.png\t\"Locomotion tasks\"\n[navigation]: ./icons/maze.png\t\"Navigation tasks\"\n[model-based]: ./icons/model.png\t\"Model-based RL\"\n[multi-agent-rl]: ./icons/marl.png\t\"Multi-Agent RL\"\n[self-play]: ./icons/sp.png\t\"Self-Play RL\"\n[evolution]: ./icons/evo.png\t\"Evolutionary \u0026 Genetic Algorithms\"\n[generalization]: ./icons/gener.png\t\"Generalization across environments\"\n[plan]: ./icons/plan.png\t\"Complex Planning Problems\"\n[transfer]: ./icons/transfer.png\t\"Transfer Learning\"\n[inverse-rl]: ./icons/irl.png\t\"Inverse Reinforcement Learning\"\n[meta-learning]: ./icons/meta.png\t\"Meta-Learning\"\n[exploration]: ./icons/sparse.png\t\"Curiosity learning, Exploration\"\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/dbobrenko%2Fawesome-rl/projects"}