{"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/don-t-do-what-doesn-t-matter-intrinsic","title":"Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness","arxiv_id":"2105.09992","date":"2021-05-20","proceeding":null,"authors":["Mathieu Seurin","Florian Strub","Philippe Preux","Olivier Pietquin"],"abstract":"Sparse rewards are double-edged training signals in reinforcement learning: easy to design but hard to optimize. Intrinsic motivation guidances have thus been developed toward alleviating the resulting exploration problem. They usually incentivize agents to look for new states through novelty signals. Yet, such methods encourage exhaustive exploration of the state space rather than focusing on the environment's salient interaction opportunities. We propose a new exploration method, called Don't Do What Doesn't Matter (DoWhaM), shifting the emphasis from state novelty to state with relevant actions. While most actions consistently change the state when used, \\textit{e.g.} moving the agent, some actions are only effective in specific states, \\textit{e.g.}, \\emph{opening} a door, \\emph{grabbing} an object. DoWhaM detects and rewards actions that seldom affect the environment. We evaluate DoWhaM on the procedurally-generated environment MiniGrid, against state-of-the-art methods and show that DoWhaM greatly reduces sample complexity.","url_abs":"https://arxiv.org/abs/2105.09992v2","url_pdf":"https://arxiv.org/pdf/2105.09992v2.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":"don-t-do-what-doesn-t-matter-intrinsic","repo_url":"https://github.com/Mathieu-Seurin/impact-driven-exploration","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2105.09992","atlas_url":"https://app.syntology.ai/?focus=2105.09992","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.09992"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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":"deterministic:regex_extraction","url":"https://github.com/Mathieu-Seurin/impact-driven-exploration","reach":null}],"summary":{"ran_draft_wrong":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"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":3,"samples":[{"code_sha256_prefix":"bc99543cb43c7aaa","entry":"compute_forward_dynamics_loss","repo":"Mathieu-Seurin/impact-driven-exploration","repo_kind":"official","path":"src/algos/ride.py","file_url":"https://github.com/Mathieu-Seurin/impact-driven-exploration/blob/HEAD/src/algos/ride.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"bc99543cb43c7aaa"}},{"code_sha256_prefix":"93f48c22fd415b3d","entry":"compute_inverse_dynamics_loss","repo":"Mathieu-Seurin/impact-driven-exploration","repo_kind":"official","path":"src/algos/ride.py","file_url":"https://github.com/Mathieu-Seurin/impact-driven-exploration/blob/HEAD/src/algos/ride.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"93f48c22fd415b3d"}},{"code_sha256_prefix":"c96a61a23006629e","entry":"learn","repo":"Mathieu-Seurin/impact-driven-exploration","repo_kind":"official","path":"src/algos/ride.py","file_url":"https://github.com/Mathieu-Seurin/impact-driven-exploration/blob/HEAD/src/algos/ride.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"mcp_get_code":{"code_sha256":"c96a61a23006629e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}