{"id":19068697,"url":"https://github.com/machine-learning-tokyo/deep_reinforcement_learning","last_synced_at":"2025-07-03T03:04:36.608Z","repository":{"id":96775557,"uuid":"184706525","full_name":"Machine-Learning-Tokyo/Deep_Reinforcement_Learning","owner":"Machine-Learning-Tokyo","description":"Resources, papers, tutorials","archived":false,"fork":false,"pushed_at":"2020-04-18T06:28:14.000Z","size":1062,"stargazers_count":124,"open_issues_count":0,"forks_count":21,"subscribers_count":16,"default_branch":"master","last_synced_at":"2025-02-22T03:31:45.464Z","etag":null,"topics":["deep-reinforcement-learning","reinforcement-learning","resources"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Machine-Learning-Tokyo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2019-05-03T06:06:39.000Z","updated_at":"2025-01-11T03:36:27.000Z","dependencies_parsed_at":null,"dependency_job_id":"6d3e53ca-02cc-42e0-a3db-176a047f891d","html_url":"https://github.com/Machine-Learning-Tokyo/Deep_Reinforcement_Learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Machine-Learning-Tokyo/Deep_Reinforcement_Learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FDeep_Reinforcement_Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FDeep_Reinforcement_Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FDeep_Reinforcement_Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FDeep_Reinforcement_Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Machine-Learning-Tokyo","download_url":"https://codeload.github.com/Machine-Learning-Tokyo/Deep_Reinforcement_Learning/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FDeep_Reinforcement_Learning/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263250596,"owners_count":23437288,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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"}},"keywords":["deep-reinforcement-learning","reinforcement-learning","resources"],"created_at":"2024-11-09T01:11:22.077Z","updated_at":"2025-07-03T03:04:36.586Z","avatar_url":"https://github.com/Machine-Learning-Tokyo.png","language":null,"readme":"# Deep Reinforcement Learning\n\n### Introduction to Reinforcement Learning with David Silver, DeepMind\nWatch the lectures from DeepMind research lead David Silver's course on reinforcement learning, taught at University College London.\n\n[[Video lectures]](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)\n\n- Lecture 1: Introduction to Reinforcement Learning\n- Lecture 2: Markov Decision Processes\n- Lecture 3: Planning by Dynamic Programming\n- Lecture 4: Model-Free Prediction\n- Lecture 5: Model-Free Control\n- Lecture 6: Value Function Approximation\n- Lecture 7: Policy Gradient Methods\n- Lecture 8: Integrating Learning and Planning\n- Lecture 9: Exploration and Exploitation\n- Lecture 10: Case Study: RL in Classic Games\n\n### Deep Reinforcement Learning: A Brief Survey\nKai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath\n\n- [[Paper]](https://arxiv.org/abs/1708.05866)\n- [IEEE Signal Processing Magazine | November 2017](https://ieeexplore.ieee.org/document/8103164)\n\n[\u003cp align=\"center\"\u003e\u003cimg src=\"https://github.com/Machine-Learning-Tokyo/Deep_Reinforcement_Learning/blob/master/deep_rl_survey.png\" width=\"600\"\u003e\u003c/p\u003e](https://arxiv.org/abs/1708.05866)\n\n\n### Spinning Up in Deep RL\nEducational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). It includes the following resources:\n\n* a short [introduction](https://spinningup.openai.com/en/latest/spinningup/rl_intro.html) to RL terminology, kinds of algorithms, and basic theory,\n* an [essay](https://spinningup.openai.com/en/latest/spinningup/spinningup.html) about how to grow into an RL research role,\n* a [curated list](https://spinningup.openai.com/en/latest/spinningup/keypapers.html) of important papers organized by topic,\n* a well-documented [code repo](https://github.com/openai/spinningup) of short, standalone implementations of key algorithms,\n* and a few [exercises](https://spinningup.openai.com/en/latest/spinningup/exercises.html) to serve as warm-ups.\n\n[[Webpage]](https://spinningup.openai.com)\n\n### Stanford CS234: Reinforcement Learning\n\nLecture Series. Stanford CS234: Reinforcement Learning (Winter 2019)  - with Prof. Emma Brunskill\n\n[[YouTube]](https://www.youtube.com/watch?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u\u0026v=FgzM3zpZ55o)\n\n### An Introduction to Deep Reinforcement Learning (2018)\nVincent Francois-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau\n\n[[PDF Book manuscript, Nov 2018]](https://arxiv.org/abs/1811.12560)\n\n### CS294-112 Deep Reinforcement Learning\n\nLecture Series. UC Berkeley. Fall 2018. \n\nInstructor : Sergey Levine\n\n[Webpage](http://rail.eecs.berkeley.edu/deeprlcourse/)\n[Youtube](https://www.youtube.com/playlist?list=PLkFD6_40KJIxJMR-j5A1mkxK26gh_qg37)\n\n\n### CS885 Reinforcement Learning\nLecture Series. University of Waterloo. Spring 2018\n\nInstructor: Pascal Poupart\n\n[Webpage](https://cs.uwaterloo.ca/~ppoupart/teaching/cs885-spring18/)\n[Youtube](https://www.youtube.com/playlist?list=PLdAoL1zKcqTXFJniO3Tqqn6xMBBL07EDc)\n\n### Advanced Deep Learning \u0026 Reinforcement Learning\n\nDeepmind 2018.\n\n[Youtube](https://www.youtube.com/watch?v=iOh7QUZGyiU\u0026list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs)\n\n### RLSS 2018\nToronto 2018.\n\n[Videos](http://videolectures.net/DLRLsummerschool2018_toronto/)\n\n### RLSS 2017\nMontreal 2017.\n\n[Videos](http://videolectures.net/deeplearning2017_montreal/)\n\n### Deep RL Bootcamp\nBerkeley CA. Aug 2017\n\n[Slides \u0026 Videos](https://sites.google.com/view/deep-rl-bootcamp/lectures)\n \n### Introduction to Reinforcement Learning\nDeepMind, 2015\n\nInstructor : David Silver\n\n[Youtube](https://www.youtube.com/watch?v=2pWv7GOvuf0\u0026list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)\n\n\n### Deep RL Bootcamp, Berkeley (2017)\nBy Pieter Abbeel, Chelsea Finn, Peter Chen, Andrej Karpathy et al.\n\n[[Webpage]](https://sites.google.com/view/deep-rl-bootcamp/lectures)\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"https://github.com/Machine-Learning-Tokyo/Deep_Reinforcement_Learning/blob/master/deep_rl_bootcamp.png\" width=\"1000\"\u003e\n\u003c/p\u003e\n\n### Reinforcement Learning Book\nWritten by [Richard Sutton](http://incompleteideas.net/index.html) and [Andrew Barto](http://www-anw.cs.umass.edu/~barto/). \n\n[[Webpage]](http://incompleteideas.net/book/the-book-2nd.html) [[PDF]](http://incompleteideas.net/book/RLbook2018.pdf) [[Goodreads]](https://www.goodreads.com/book/show/39813875-reinforcement-learning)\n\n### Denny Britz: Reinforcement Learning\nImplementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course. All code is written in Python 3 and uses RL environments from OpenAI Gym. Advanced techniques use Tensorflow for neural network implementations.\n\n[[GitHub]](https://github.com/dennybritz/reinforcement-learning)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmachine-learning-tokyo%2Fdeep_reinforcement_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmachine-learning-tokyo%2Fdeep_reinforcement_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmachine-learning-tokyo%2Fdeep_reinforcement_learning/lists"}