{"id":"https://openalex.org/W7162645050","doi":"https://doi.org/10.48550/arxiv.2605.28276","title":"Commit to the Bit: Reactive Reinforcement Learning Done Right","display_name":"Commit to the Bit: Reactive Reinforcement Learning Done Right","publication_year":2026,"publication_date":"2026-05-27","ids":{"openalex":"https://openalex.org/W7162645050","doi":"https://doi.org/10.48550/arxiv.2605.28276"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.28276","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28276","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.28276","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5098937210","display_name":"Onno Eberhard","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eberhard, Onno","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088066921","display_name":"Claire Vernade","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vernade, Claire","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5049845074","display_name":"Michael Muehlebach","orcid":"https://orcid.org/0000-0002-7764-3069"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muehlebach, Michael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9448999762535095,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9448999762535095,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.010999999940395355,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.008100000210106373,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/commit","display_name":"Commit","score":0.8115000128746033},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7958999872207642},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.669700026512146},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.5855000019073486},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5609999895095825},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.49540001153945923},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.49079999327659607},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.44130000472068787}],"concepts":[{"id":"https://openalex.org/C153180980","wikidata":"https://www.wikidata.org/wiki/Q19776675","display_name":"Commit","level":2,"score":0.8115000128746033},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7958999872207642},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.669700026512146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5856000185012817},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.5855000019073486},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5609999895095825},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.49540001153945923},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.49079999327659607},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.44130000472068787},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.42399999499320984},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3986999988555908},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.3873000144958496},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3871000111103058},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C176248197","wikidata":"https://www.wikidata.org/wiki/Q458526","display_name":"Probably approximately correct learning","level":4,"score":0.3107999861240387},{"id":"https://openalex.org/C55479107","wikidata":"https://www.wikidata.org/wiki/Q97663916","display_name":"Stochastic approximation","level":3,"score":0.3091999888420105},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2976999878883362},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2712000012397766},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.2662999927997589},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.25189998745918274},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.28276","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28276","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.28276","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.28276","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7953363656997681,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reinforcement":[0],"learning":[1,43],"algorithms":[2],"are":[3,21],"commonly":[4],"analyzed":[5],"(and":[6],"designed)":[7],"under":[8,76],"the":[9,31,40,72,89,106,123,134],"Markov":[10],"assumption.":[11],"This":[12,83],"is":[13,85,98,133],"unrealistic,":[14],"as":[15],"most":[16],"environments":[17],"encountered":[18],"in":[19,48,93,104],"practice":[20],"either":[22],"partially":[23],"observable,":[24],"or":[25],"require":[26],"function":[27],"approximation":[28],"that":[29],"restricts":[30],"agent":[32],"to":[33,71,110],"access":[34],"non-Markovian":[35],"state":[36,58],"features.":[37],"We":[38,60],"consider":[39],"problem":[41],"of":[42,101,130,136],"an":[44,77],"optimal":[45,73],"reactive":[46,74],"policy":[47,75,108],"a":[49,62,99,111,116],"finite":[50],"environment":[51],"with":[52],"deterministic":[53],"observations":[54],"(or":[55],"equivalently,":[56],"hard":[57],"aggregation).":[59],"introduce":[61],"new":[63],"algorithm,":[64],"Committed":[65],"Q-learning,":[66],"and":[67,118],"prove":[68],"almost-sure":[69],"convergence":[70],"intuitive":[78],"assumption":[79,84],"we":[80],"call":[81],"rewire-robustness.":[82],"strictly":[86],"weaker":[87],"than":[88],"$q_\\star$-realizability":[90],"condition":[91],"used":[92],"prior":[94],"work.":[95],"Our":[96],"algorithm":[97],"variant":[100],"classical":[102],"Q-learning":[103],"which":[105],"behavior":[107],"commits":[109],"single":[112],"action":[113],"upon":[114],"entering":[115],"feature,":[117],"only":[119],"resamples":[120],"actions":[121],"when":[122],"observed":[124],"feature":[125],"changes.":[126],"A":[127],"crucial":[128],"part":[129],"our":[131],"analysis":[132],"introduction":[135],"quasi-Markov":[137],"environments.":[138]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-29T00:00:00"}