{"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/exploration-via-planning-for-information","title":"Exploration via Planning for Information about the Optimal Trajectory","arxiv_id":"2210.04642","date":"2022-10-06","proceeding":null,"authors":["Viraj Mehta","Ian Char","Joseph Abbate","Rory Conlin","Mark D. Boyer","Stefano Ermon","Jeff Schneider","Willie Neiswanger"],"abstract":"Many potential applications of reinforcement learning (RL) are stymied by the large numbers of samples required to learn an effective policy. This is especially true when applying RL to real-world control tasks, e.g. in the sciences or robotics, where executing a policy in the environment is costly. In popular RL algorithms, agents typically explore either by adding stochasticity to a reward-maximizing policy or by attempting to gather maximal information about environment dynamics without taking the given task into account. In this work, we develop a method that allows us to plan for exploration while taking both the task and the current knowledge about the dynamics into account. The key insight to our approach is to plan an action sequence that maximizes the expected information gain about the optimal trajectory for the task at hand. We demonstrate that our method learns strong policies with 2x fewer samples than strong exploration baselines and 200x fewer samples than model free methods on a diverse set of low-to-medium dimensional control tasks in both the open-loop and closed-loop control settings.","url_abs":"https://arxiv.org/abs/2210.04642v1","url_pdf":"https://arxiv.org/pdf/2210.04642v1.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":"exploration-via-planning-for-information","repo_url":"https://github.com/fusion-ml/trajectory-information-rl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"exploration-via-planning-for-information","repo_url":"https://github.com/rehoss/lbmpc_semimarkov","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2210.04642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.04642"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rehoss/lbmpc_semimarkov","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/fusion-ml/trajectory-information-rl","reach":null}],"summary":{"ran":6,"unverified":2},"by_repo_kind":{"listed":{"samples":8,"ran":6,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"80237591e7b4d3eb","entry":"AcqFunction","repo":"rehoss/lbmpc_semimarkov","repo_kind":"listed","path":"lbmpc_semimarkov/acq/acquisition.py","file_url":"https://github.com/rehoss/lbmpc_semimarkov/blob/HEAD/lbmpc_semimarkov/acq/acquisition.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"80237591e7b4d3eb"}},{"code_sha256_prefix":"85c1f25773a865b6","entry":"AlgorithmSet","repo":"rehoss/lbmpc_semimarkov","repo_kind":"listed","path":"lbmpc_semimarkov/acq/acquisition.py","file_url":"https://github.com/rehoss/lbmpc_semimarkov/blob/HEAD/lbmpc_semimarkov/acq/acquisition.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"85c1f25773a865b6"}},{"code_sha256_prefix":"fa3292dc4951b1c6","entry":"Base","repo":"rehoss/lbmpc_semimarkov","repo_kind":"listed","path":"lbmpc_semimarkov/acq/acquisition.py","file_url":"https://github.com/rehoss/lbmpc_semimarkov/blob/HEAD/lbmpc_semimarkov/acq/acquisition.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"fa3292dc4951b1c6"}},{"code_sha256_prefix":"abd4eaae6be6d5b6","entry":"BatchAlgorithmSet","repo":"rehoss/lbmpc_semimarkov","repo_kind":"listed","path":"lbmpc_semimarkov/acq/acquisition.py","file_url":"https://github.com/rehoss/lbmpc_semimarkov/blob/HEAD/lbmpc_semimarkov/acq/acquisition.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"abd4eaae6be6d5b6"}},{"code_sha256_prefix":"095a77eeda8d0fd2","entry":"FunctionSample","repo":"rehoss/lbmpc_semimarkov","repo_kind":"listed","path":"lbmpc_semimarkov/acq/acquisition.py","file_url":"https://github.com/rehoss/lbmpc_semimarkov/blob/HEAD/lbmpc_semimarkov/acq/acquisition.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"095a77eeda8d0fd2"}},{"code_sha256_prefix":"0ab042ab92eeb876","entry":"Timer","repo":"rehoss/lbmpc_semimarkov","repo_kind":"listed","path":"lbmpc_semimarkov/acq/acquisition.py","file_url":"https://github.com/rehoss/lbmpc_semimarkov/blob/HEAD/lbmpc_semimarkov/acq/acquisition.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0ab042ab92eeb876"}},{"code_sha256_prefix":"9c847fe3e42ed3b1","entry":"AlgoAcqFunction","repo":"rehoss/lbmpc_semimarkov","repo_kind":"listed","path":"lbmpc_semimarkov/acq/acquisition.py","file_url":"https://github.com/rehoss/lbmpc_semimarkov/blob/HEAD/lbmpc_semimarkov/acq/acquisition.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9c847fe3e42ed3b1"}},{"code_sha256_prefix":"3da2fb95b2eeaacf","entry":"BaxAcqFunction","repo":"rehoss/lbmpc_semimarkov","repo_kind":"listed","path":"lbmpc_semimarkov/acq/acquisition.py","file_url":"https://github.com/rehoss/lbmpc_semimarkov/blob/HEAD/lbmpc_semimarkov/acq/acquisition.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3da2fb95b2eeaacf"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}