{"id":22110742,"url":"https://github.com/vmayoral/basic_reinforcement_learning","last_synced_at":"2025-05-16T03:04:29.764Z","repository":{"id":43459119,"uuid":"58313601","full_name":"vmayoral/basic_reinforcement_learning","owner":"vmayoral","description":"An introductory series to Reinforcement Learning (RL) with comprehensive step-by-step tutorials.","archived":false,"fork":false,"pushed_at":"2023-07-14T07:49:28.000Z","size":45200,"stargazers_count":1142,"open_issues_count":3,"forks_count":362,"subscribers_count":60,"default_branch":"master","last_synced_at":"2025-04-08T13:11:14.345Z","etag":null,"topics":["ai","artificial-intelligence","deep-learning","deeplearning","neural-networks","openai-gym","q-learning","reinforcement-learning","tutorial"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/vmayoral.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","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":"2016-05-08T13:34:29.000Z","updated_at":"2025-04-08T09:30:05.000Z","dependencies_parsed_at":"2024-12-01T10:20:53.432Z","dependency_job_id":null,"html_url":"https://github.com/vmayoral/basic_reinforcement_learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vmayoral%2Fbasic_reinforcement_learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vmayoral%2Fbasic_reinforcement_learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vmayoral%2Fbasic_reinforcement_learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vmayoral%2Fbasic_reinforcement_learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vmayoral","download_url":"https://codeload.github.com/vmayoral/basic_reinforcement_learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254459088,"owners_count":22074605,"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":["ai","artificial-intelligence","deep-learning","deeplearning","neural-networks","openai-gym","q-learning","reinforcement-learning","tutorial"],"created_at":"2024-12-01T10:10:46.866Z","updated_at":"2025-05-16T03:04:24.753Z","avatar_url":"https://github.com/vmayoral.png","language":"Jupyter Notebook","readme":"Basic Reinforcement Learning (RL)\n============================\n\nThis repository aims to provide an introduction series to reinforcement learning (RL) by delivering a walkthough on how to code different RL techniques.\n\n### Background review\nA quick background review of RL is available [here](BACKGROUND.md).\n\n### Tutorials:\n- [x] Tutorial 1: [Q-learning](tutorial1/README.md)\n- [x] Tutorial 2: [SARSA](tutorial2/README.md)\n- [x] Tutorial 3: [Exploring OpenAI gym](tutorial3/README.md)\n- [x] Tutorial 4: [Q-learning in OpenAI gym](tutorial4/README.md)\n- [x] Tutorial 5: [Deep Q-learning (DQN)](tutorial5/README.md)\n- [x] Tutorial 6: [Deep Convolutional Q-learning](tutorial6/README.md)\n- [x] Tutorial 7: [Reinforcement Learning with ROS and Gazebo](tutorial7/README.md)\n- [ ] ~~Tutorial 8: [Reinforcement Learning in DOOM](tutorial8/README.md)~~ (**unfinished**)\n- [x] Tutorial 9: [Deep Deterministic Policy Gradients (DDPG)](tutorial9/README.md)\n- [ ] ~~Tutorial 10: [Guided Policy Search (GPS)](tutorial10/README.md)~~ (**unfinished**)\n- [ ] Tutorial 11: [A review of different AI techniques for RL](tutorial11/README.md) (**WIP**)\n- [x] Tutorial 12: [Reviewing Policy Gradient methods](tutorial12/README.md)\n- [ ] ~~Tutorial 13: [Continuous-state spaces with DQN](tutorial13/README.md)~~ (**merged**)\n- [x] Tutorial 14: [Benchmarking RL techniques](tutorial14/README.md)\n- [ ] ~~Tutorial 15: [Reviewing Vanilla Policy Gradient (VPG)](tutorial15/README.md)~~ (**failed miserably**)\n\n### References:\n- Chris Watkins, Learning from Delayed Rewards, Cambridge, 1989 ([thesis](http://www.cs.rhul.ac.uk/home/chrisw/new_thesis.pdf))\n- Awesome Reinforcement Learning repository, https://github.com/aikorea/awesome-rl\n- Reinforcement learning CS9417ML, School of Computer Science \u0026 Engineering, UNSW Sydney, http://www.cse.unsw.edu.au/~cs9417ml/RL1/index.html\n- Reinforcement learning blog posts, https://studywolf.wordpress.com/2012/11/25/reinforcement-learning-q-learning-and-exploration/\n- OpenAI gym docs, https://gym.openai.com/docs\n- Vincent Bons implementations, https://gist.github.com/wingedsheep\n- David Silver's Deep Reinforcement Learning talk, http://videolectures.net/rldm2015_silver_reinforcement_learning/\n- Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., \u0026 Zaremba, W. (2016). OpenAI Gym. arXiv preprint arXiv:1606.01540.\n- https://sites.google.com/view/deep-rl-bootcamp/lectures\n- https://github.com/vmayoral/gym-cryptocurrencies\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvmayoral%2Fbasic_reinforcement_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvmayoral%2Fbasic_reinforcement_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvmayoral%2Fbasic_reinforcement_learning/lists"}