{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/regret-minimization-for-partially-observable","title":"Regret Minimization for Partially Observable Deep Reinforcement Learning","arxiv_id":"1710.11424","date":"2017-10-31","proceeding":"ICML 2018 7","authors":["Peter Jin","Kurt Keutzer","Sergey Levine"],"abstract":"Deep reinforcement learning algorithms that estimate state and state-action\nvalue functions have been shown to be effective in a variety of challenging\ndomains, including learning control strategies from raw image pixels. However,\nalgorithms that estimate state and state-action value functions typically\nassume a fully observed state and must compensate for partial observations by\nusing finite length observation histories or recurrent networks. In this work,\nwe propose a new deep reinforcement learning algorithm based on counterfactual\nregret minimization that iteratively updates an approximation to an\nadvantage-like function and is robust to partially observed state. We\ndemonstrate that this new algorithm can substantially outperform strong\nbaseline methods on several partially observed reinforcement learning tasks:\nlearning first-person 3D navigation in Doom and Minecraft, and acting in the\npresence of partially observed objects in Doom and Pong.","url_abs":"http://arxiv.org/abs/1710.11424v2","url_pdf":"http://arxiv.org/pdf/1710.11424v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"regret-minimization-for-partially-observable","repo_url":"https://github.com/peterhj/arm-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-reinforcement-learning","task_name":"Deep Reinforcement Learning"},{"task_slug":"minecraft","task_name":"Minecraft"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":null,"task_name":"counterfactual"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1710.11424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1710.11424"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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