{"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/near-optimal-behavior-via-approximate-state","title":"Near Optimal Behavior via Approximate State Abstraction","arxiv_id":"1701.04113","date":"2017-01-15","proceeding":null,"authors":["David Abel","D. Ellis Hershkowitz","Michael L. Littman"],"abstract":"The combinatorial explosion that plagues planning and reinforcement learning\n(RL) algorithms can be moderated using state abstraction. Prohibitively large\ntask representations can be condensed such that essential information is\npreserved, and consequently, solutions are tractably computable. However, exact\nabstractions, which treat only fully-identical situations as equivalent, fail\nto present opportunities for abstraction in environments where no two\nsituations are exactly alike. In this work, we investigate approximate state\nabstractions, which treat nearly-identical situations as equivalent. We present\ntheoretical guarantees of the quality of behaviors derived from four types of\napproximate abstractions. Additionally, we empirically demonstrate that\napproximate abstractions lead to reduction in task complexity and bounded loss\nof optimality of behavior in a variety of environments.","url_abs":"http://arxiv.org/abs/1701.04113v1","url_pdf":"http://arxiv.org/pdf/1701.04113v1.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":"near-optimal-behavior-via-approximate-state","repo_url":"https://github.com/david-abel/state_abstraction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.04113","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}