{"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/structure-and-randomness-in-planning-and","title":"Structure and randomness in planning and reinforcement learning","arxiv_id":null,"date":"2021-01-01","proceeding":"NeurIPS Workshop LMCA 2020 12","authors":["Piotr Kozakowski","Piotr Januszewski","Konrad Czechowski","Łukasz Kuciński","Piotr Miłoś"],"abstract":"Planning in large state spaces inevitably needs to balance depth and breadth of the search. It has a crucial impact on planners performance and most manage this interplay implicitly. We present a novel method $\\textit{Shoot Tree Search (STS)}$, which makes it possible to control this trade-off more explicitly. Our algorithm can be understood as an interpolation between two celebrated search mechanisms: MCTS and random shooting. It also lets the user control the bias-variance trade-off, akin to $TD(n)$, but in the tree search context.\n\nIn experiments on challenging domains, we show that STS can get the best of both worlds consistently achieving higher scores. ","url_abs":"https://openreview.net/forum?id=UOOmHiXetC","url_pdf":"https://openreview.net/pdf?id=UOOmHiXetC","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":"structure-and-randomness-in-planning-and","repo_url":"https://github.com/shoot-tree-search/sts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"jax","reach":null}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"sts","task_name":"STS"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}