{"id":13503583,"url":"https://github.com/debmandal/RL-Causality","last_synced_at":"2025-03-29T18:31:22.202Z","repository":{"id":172413752,"uuid":"160588393","full_name":"debmandal/RL-Causality","owner":"debmandal","description":"References at the Intersection of Causality and Reinforcement Learning","archived":false,"fork":false,"pushed_at":"2020-08-19T18:52:28.000Z","size":17,"stargazers_count":88,"open_issues_count":1,"forks_count":10,"subscribers_count":10,"default_branch":"master","last_synced_at":"2024-11-01T00:31:46.971Z","etag":null,"topics":["reference"],"latest_commit_sha":null,"homepage":"","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/debmandal.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":"2018-12-05T22:48:39.000Z","updated_at":"2024-08-04T17:12:53.000Z","dependencies_parsed_at":null,"dependency_job_id":"ae42e07f-92eb-4a67-94dd-d7a01034c974","html_url":"https://github.com/debmandal/RL-Causality","commit_stats":null,"previous_names":["debmandal/rl-causality"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/debmandal%2FRL-Causality","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/debmandal%2FRL-Causality/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/debmandal%2FRL-Causality/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/debmandal%2FRL-Causality/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/debmandal","download_url":"https://codeload.github.com/debmandal/RL-Causality/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246226990,"owners_count":20743866,"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":["reference"],"created_at":"2024-07-31T23:00:40.856Z","updated_at":"2025-03-29T18:31:22.198Z","avatar_url":"https://github.com/debmandal.png","language":null,"funding_links":[],"categories":["Topics","Related Repos","🚀 GitHub Repositories"],"sub_categories":["Research Paper","🌟 **Real-World Magic**"],"readme":"I aim to review and understand how causal inference can be helpful in making reinforcement learning better. I think causality can make RL more sample efficient, make it interpretable and broaden its range of applications.\n\n# Papers at the Intersection of Reinforcement Learning and Causal Inference\n\n* [Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search](https://openreview.net/forum?id=BJG0voC9YQ), Under Review, *ICLR*, 2019.\n* [Causal Reasoning from Meta-Reinforcement Learning](https://openreview.net/forum?id=H1ltQ3R9KQ), Under Review, *ICLR*, 2019.\n* [Discovering Latent Causes in Reinforcement Learning](https://www.princeton.edu/~nivlab/papers/GershmanNormanNiv2015.pdf), Gershman et al., *Behavioral Sciences*, 2015.\n* [Reinforcement Learning and Cauasl Models](http://gershmanlab.webfactional.com/pubs/RL_causal.pdf), Sam Gershman, 2016.\n* [Representation Balancing MDPs for Off-Policy Policy Evaluation](https://arxiv.org/pdf/1805.09044.pdf), Liu et. al., *NeurIPS*, 2018.\n* [Learning Plannable Representation with Causal InfoGAN](https://arxiv.org/pdf/1807.09341.pdf), Kurutach et al., *PAL*, 2018.\n* [High-Confidence Policy Improvement](https://people.cs.umass.edu/~pthomas/papers/Thomas2015b.pdf), Thomas et al., *ICML*, 2015.\n\n## Relevant RL Papers\n\n* [Learning Model Based Planning from Scratch](https://arxiv.org/pdf/1707.06170.pdf), Pascanu et al., *arxiv*, 2017.\n* [An Introduction to Deep Reinforcement Learning](https://arxiv.org/pdf/1811.12560.pdf), Francois-Lavet et al., *arxiv*, 2018.\n* [Combined Reinforcement Learning via Abstract Representations](https://arxiv.org/abs/1809.04506), Francois-Lavet et al., *arxiv*, 2018.\n* [TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning](https://arxiv.org/abs/1710.11417), Farquhar et al., *arxiv*, 2018.\n* [Hindsight Experience Replay](https://papers.nips.cc/paper/7090-hindsight-experience-replay.pdf),  Andrychowicz et al., *NIPS*, 2017. \n* [Universal Value Function Approximator](http://proceedings.mlr.press/v37/schaul15.pdf), Schaul et al., *ICML*, 2015.\n* [Continuous Control with Deep Reinforcement Learning](https://arxiv.org/pdf/1509.02971.pdf), Lillicrap et al., *ICLR*, 2016.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdebmandal%2FRL-Causality","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdebmandal%2FRL-Causality","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdebmandal%2FRL-Causality/lists"}