{"id":"https://openalex.org/W7171567194","doi":"https://doi.org/10.48550/arxiv.2607.23930","title":"Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping","display_name":"Bridging Reinforcement Learning and Optimal Control via Feasible Action Mapping","publication_year":2026,"publication_date":"2026-07-27","ids":{"openalex":"https://openalex.org/W7171567194","doi":"https://doi.org/10.48550/arxiv.2607.23930"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.23930","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.23930","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.23930","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134225692","display_name":"Stefan Richter","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Richter, Stefan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143866530","display_name":"Alberto Giammarino","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Giammarino, Alberto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044302210","display_name":"Guillem Torrente","orcid":"https://orcid.org/0000-0003-2839-2774"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Torrente, Guillem","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065665155","display_name":"Sam Blakeman","orcid":"https://orcid.org/0000-0002-5023-7933"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Blakeman, Sam","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5102884528","display_name":"Peter D\u00fcrr","orcid":"https://orcid.org/0000-0002-3840-5009"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"D\u00fcrr, Peter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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":0.6929000020027161,"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":0.6929000020027161,"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/T12794","display_name":"Adaptive Dynamic Programming Control","score":0.19269999861717224,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10653","display_name":"Robot Manipulation and Learning","score":0.02879999950528145,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6502000093460083},{"id":"https://openalex.org/keywords/optimal-control","display_name":"Optimal control","score":0.6166999936103821},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5835999846458435},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.5478000044822693},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.5216000080108643},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.48010000586509705},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.45840001106262207},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.4018000066280365}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6502000093460083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6169999837875366},{"id":"https://openalex.org/C91575142","wikidata":"https://www.wikidata.org/wiki/Q1971426","display_name":"Optimal control","level":2,"score":0.6166999936103821},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5835999846458435},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.5478000044822693},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5216000080108643},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.517799973487854},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.48010000586509705},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.45840001106262207},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.4018000066280365},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.39329999685287476},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.37049999833106995},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C79379906","wikidata":"https://www.wikidata.org/wiki/Q3174497","display_name":"Dynamical systems theory","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C44616089","wikidata":"https://www.wikidata.org/wiki/Q30158686","display_name":"Constraint satisfaction","level":3,"score":0.29330000281333923},{"id":"https://openalex.org/C55660270","wikidata":"https://www.wikidata.org/wiki/Q5164377","display_name":"Constrained optimization","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.2806999981403351},{"id":"https://openalex.org/C17500928","wikidata":"https://www.wikidata.org/wiki/Q959968","display_name":"Control system","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2676999866962433},{"id":"https://openalex.org/C37404715","wikidata":"https://www.wikidata.org/wiki/Q380679","display_name":"Dynamic programming","level":2,"score":0.2635999917984009}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.23930","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.23930","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.23930","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.23930","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Operating":[0],"constrained":[1],"dynamical":[2,81],"systems":[3],"requires":[4],"controllers":[5],"to":[6,100,130,137],"efficiently":[7],"solve":[8],"complex":[9],"tasks":[10],"while":[11],"enforcing":[12],"recursive":[13],"feasibility":[14],"and":[15,39,118,163],"safety":[16,90],"constraints.":[17,83],"To":[18],"address":[19],"these":[20,147],"competing":[21],"requirements,":[22],"we":[23,152],"present":[24],"Feasible":[25],"Action":[26],"for":[27,141],"Optimal":[28,40,72],"Control":[29,41,73],"(FAOC),":[30],"a":[31,47,60,65],"novel":[32],"control":[33],"framework":[34],"integrating":[35],"Reinforcement":[36],"Learning":[37],"(RL)":[38],"(OC).":[42],"The":[43],"key":[44],"contribution":[45],"is":[46,122],"computationally":[48],"efficient,":[49],"optimization-based":[50],"mapping":[51],"algorithm":[52],"that":[53,154],"transforms":[54],"the":[55,71,80,88,94,103,108,119,126],"RL":[56,109,129],"agent's":[57],"action":[58,105],"from":[59],"static":[61],"abstract":[62,104],"set":[63,69],"into":[64],"state-dependent":[66],"feasible":[67],"parameter":[68],"of":[70,79,91,96,107,128],"Problem":[74],"(OCP),":[75],"guaranteeing":[76],"strict":[77],"satisfaction":[78],"system's":[82],"Thus,":[84],"FAOC":[85,155],"effectively":[86],"combines":[87],"predictable":[89],"OC":[92],"with":[93],"flexibility":[95],"RL.":[97],"In":[98],"contrast":[99],"prior":[101],"work,":[102],"space":[106],"agent":[110],"does":[111],"not":[112,123],"require":[113],"expert":[114],"or":[115],"heuristic":[116],"design,":[117],"OCP":[120],"formulation":[121],"compromised":[124],"by":[125],"inability":[127],"guarantee":[131],"feasibility.":[132],"We":[133],"apply":[134],"our":[135],"approach":[136],"real-time":[138],"motion":[139],"planning":[140],"robot":[142],"table":[143],"tennis,":[144],"which":[145],"encapsulates":[146],"challenges.":[148],"Via":[149],"simulated":[150],"experiments,":[151],"show":[153],"outperforms":[156],"state-of-the-art":[157],"baselines":[158],"in":[159],"both":[160],"sample":[161],"efficiency":[162],"closed-loop":[164],"performance.":[165]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-07-29T00:00:00"}