{"id":"https://openalex.org/W7166580516","doi":"https://doi.org/10.48550/arxiv.2606.28152","title":"Regularized Reward-Punishment Reinforcement Learning","display_name":"Regularized Reward-Punishment Reinforcement Learning","publication_year":2026,"publication_date":"2026-06-26","ids":{"openalex":"https://openalex.org/W7166580516","doi":"https://doi.org/10.48550/arxiv.2606.28152"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.28152","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28152","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":null,"license_id":null,"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.2606.28152","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139601650","display_name":"Jiexin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiexin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5031054137","display_name":"Eiji Uchibe","orcid":"https://orcid.org/0000-0001-7908-0258"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Uchibe, Eiji","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":0.7197999954223633,"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":0.7197999954223633,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.025299999862909317,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.022700000554323196,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8676999807357788},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4941999912261963},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.48410001397132874},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.45980000495910645},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.3700999915599823},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.3395000100135803}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8676999807357788},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7504000067710876},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5388000011444092},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4941999912261963},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.48410001397132874},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.45980000495910645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4138000011444092},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.33009999990463257},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.32670000195503235},{"id":"https://openalex.org/C153517567","wikidata":"https://www.wikidata.org/wiki/Q26090","display_name":"Mechanism design","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.27059999108314514}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.28152","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28152","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.28152","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.28152","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.4243738353252411,"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":{"We":[0],"propose":[1],"KL-Coupled":[2,20],"Policy":[3],"Regularization":[4],"(KCPR),":[5],"a":[6,53,85,146],"policy":[7],"coordination":[8,132],"framework":[9],"for":[10,57,94,137,150],"Reward-Punishment":[11],"Reinforcement":[12],"Learning":[13],"(RPRL).":[14],"Based":[15],"on":[16],"KCPR,":[17],"we":[18,83],"derive":[19],"Soft":[21],"Optimality":[22],"(KCSO)":[23],"and":[24,37,65,71,89,97,103,113,125,142],"develop":[25],"its":[26],"deep":[27],"realization,":[28],"klDMP.":[29],"Unlike":[30],"existing":[31],"RPRL":[32],"approaches":[33],"that":[34,109,130],"optimize":[35],"reward-seeking":[36],"punishment-related":[38,98],"policies":[39,48,64],"largely":[40],"independently,":[41],"KCPR":[42],"enables":[43],"direct":[44],"interactions":[45],"between":[46],"companion":[47],"by":[49],"treating":[50],"each":[51],"as":[52,145],"dynamically":[54],"learned":[55],"prior":[56],"the":[58],"other.":[59],"KCSO":[60],"yields":[61],"coupled":[62],"soft-optimal":[63],"KL-regularized":[66],"Bellman":[67],"operators,":[68],"allowing":[69],"reward":[70],"punishment":[72],"information":[73],"to":[74],"jointly":[75],"influence":[76],"value":[77],"propagation.":[78],"To":[79],"improve":[80],"learning":[81,114,152],"stability,":[82],"introduce":[84],"companion-prior":[86],"softening":[87],"mechanism":[88,136],"evaluate":[90],"separate":[91],"replay-buffer":[92],"designs":[93],"balancing":[95],"reward-":[96],"experience.":[99],"Experiments":[100],"in":[101],"grid-world":[102],"Gazebo":[104],"robotic":[105],"navigation":[106],"tasks":[107],"demonstrate":[108],"klDMP":[110],"improves":[111],"safety":[112],"stability":[115],"while":[116],"maintaining":[117],"competitive":[118],"task":[119],"performance":[120],"compared":[121],"with":[122,154],"DQN,":[123],"SQL":[124],"softDMP.":[126],"These":[127],"results":[128],"suggest":[129],"policy-level":[131],"provides":[133],"an":[134],"effective":[135],"integrating":[138],"multiple":[139],"behavioral":[140],"objectives":[141],"may":[143],"serve":[144],"useful":[147],"design":[148],"principle":[149],"reinforcement":[151],"systems":[153],"interacting":[155],"motivational":[156],"processes.":[157]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-30T00:00:00"}