{"id":"https://openalex.org/W7102413347","doi":"https://doi.org/10.48550/arxiv.2510.24515","title":"Learning Ordinal Response Policies in Rank-Based Stochastic Prize-Collecting Games","display_name":"Learning Ordinal Response Policies in Rank-Based Stochastic Prize-Collecting Games","publication_year":2025,"publication_date":"2025-10-28","ids":{"openalex":"https://openalex.org/W7102413347","doi":"https://doi.org/10.48550/arxiv.2510.24515"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2510.24515","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.24515","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.2510.24515","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Fernando, Malintha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fernando, Malintha","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"\u00d6gren, Petter","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u00d6gren, Petter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Zhang, Silun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Silun","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":true,"primary_topic":{"id":"https://openalex.org/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.26829999685287476,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10567","display_name":"Vehicle Routing Optimization Methods","score":0.26829999685287476,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.23100000619888306,"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/T10249","display_name":"Distributed Control Multi-Agent Systems","score":0.19660000503063202,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/scheduling","display_name":"Scheduling (production processes)","score":0.5727999806404114},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5378999710083008},{"id":"https://openalex.org/keywords/nash-equilibrium","display_name":"Nash equilibrium","score":0.4796999990940094},{"id":"https://openalex.org/keywords/plan","display_name":"Plan (archaeology)","score":0.4023999869823456},{"id":"https://openalex.org/keywords/vehicle-routing-problem","display_name":"Vehicle routing problem","score":0.3970000147819519},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.3871000111103058},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.36579999327659607},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.36070001125335693},{"id":"https://openalex.org/keywords/orienteering","display_name":"Orienteering","score":0.3424000144004822}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5932000279426575},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5727999806404114},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5378999710083008},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4846000075340271},{"id":"https://openalex.org/C46814582","wikidata":"https://www.wikidata.org/wiki/Q23389","display_name":"Nash equilibrium","level":2,"score":0.4796999990940094},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.4023999869823456},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.39800000190734863},{"id":"https://openalex.org/C123784306","wikidata":"https://www.wikidata.org/wiki/Q944041","display_name":"Vehicle routing problem","level":3,"score":0.3970000147819519},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.3871000111103058},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.36070001125335693},{"id":"https://openalex.org/C513932644","wikidata":"https://www.wikidata.org/wiki/Q29358","display_name":"Orienteering","level":2,"score":0.3424000144004822},{"id":"https://openalex.org/C48243021","wikidata":"https://www.wikidata.org/wiki/Q932522","display_name":"Strategic planning","level":2,"score":0.3125999867916107},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.3077000081539154},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C32407928","wikidata":"https://www.wikidata.org/wiki/Q2733833","display_name":"Best response","level":3,"score":0.30160000920295715},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2957000136375427},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C55416958","wikidata":"https://www.wikidata.org/wiki/Q6206757","display_name":"Job shop scheduling","level":3,"score":0.26910001039505005},{"id":"https://openalex.org/C22171661","wikidata":"https://www.wikidata.org/wiki/Q1074380","display_name":"Stochastic game","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C2909711754","wikidata":"https://www.wikidata.org/wiki/Q7100785","display_name":"Ordinal Scale","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2565000057220459},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2510.24515","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.24515","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.2510.24515","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.24515","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,209],"Team":[1],"Orienteering":[2,63],"Problem":[3],"(TOP)":[4],"generalizes":[5],"many":[6,24],"real-world":[7],"multi-agent":[8,33,205],"scheduling":[9],"and":[10,20,87,95,141,159],"routing":[11,114],"tasks":[12],"that":[13,37,99,109,164,170,195],"occur":[14],"in":[15,32,56,74,107,165,204,247,250],"autonomous":[16],"mobility,":[17],"aerial":[18],"logistics,":[19],"surveillance":[21],"applications.":[22],"While":[23],"flavors":[25],"of":[26,69,77,116,128,136,221],"the":[27,39,70,75,112,126,168,182,187,191,196],"TOP":[28,71,119],"exist":[29],"for":[30],"planning":[31],"systems,":[34],"they":[35,47,54],"assume":[36],"all":[38],"agents":[40,79,222],"cooperate":[41],"toward":[42],"a":[43,82,102,133,145,200],"single":[44],"objective;":[45],"therefore,":[46],"do":[48],"not":[49],"extend":[50],"to":[51,72,216,224,243],"settings":[52,249],"when":[53,181],"compete":[55],"reward-scarce":[57],"environments.":[58],"We":[59,124],"propose":[60,125,231,233],"Stochastic":[61],"Prize-Collecting":[62],"Games":[64],"(SPCOG)":[65],"as":[66,132,199,239],"an":[67,117,137,240],"extension":[68],"plan":[73],"presence":[76],"self-interested":[78],"operating":[80],"on":[81,93,152,186,207,253],"graph,":[83],"under":[84,120,156],"energy":[85],"constraints":[86],"stochastic":[88],"transitions.":[89],"A":[90],"theoretical":[91],"discussion":[92],"complete":[94],"star":[96],"graphs":[97,155],"establishes":[98],"there":[100],"is":[101,184],"unique":[103],"pure":[104],"Nash":[105],"equilibrium":[106],"SPCOGs":[108],"coincides":[110],"with":[111,218],"optimal":[113],"solution":[115],"equivalent":[118],"rank-based":[121],"conflict":[122],"resolution.":[123],"concept":[127],"Ordinal":[129,235],"Rank":[130],"(OR)":[131],"concise":[134],"representation":[135],"agents'":[138],"global":[139,179,192],"rank":[140],"its":[142],"location":[143],"within":[144],"topological,":[146],"well-defined":[147],"neighborhood.":[148],"Empirical":[149],"evaluations":[150],"conducted":[151],"real-world,":[153],"road-network":[154],"both":[157],"dynamic":[158],"stationary":[160],"prize":[161],"distributions":[162],"show":[163],"parameter-sharing":[166],"settings,":[167],"policies":[169,177,211,246],"leverage":[171,178],"local":[172],"information":[173,180],"can":[174],"outperform":[175],"those":[176],"former":[183],"conditioned":[185,226],"OR":[188,197],"rather":[189],"than":[190],"rank,":[193],"indicating":[194],"acts":[198],"strong":[201],"inductive":[202],"bias":[203],"games":[206,217,252],"graphs.":[208,254],"OR-conditioned":[210],"also":[212,230],"generalize":[213],"much":[214],"better":[215],"large":[219],"number":[220],"compared":[223],"global-rank":[225],"policies.":[227],"Finally,":[228],"we":[229,232],"Fictitious":[234],"Response":[236],"Learning":[237],"(FORL)":[238],"entropy-regulated":[241],"algorithm":[242],"obtain":[244],"convergent":[245],"independent-learning":[248],"prize-collecting":[251]},"counts_by_year":[],"updated_date":"2026-06-12T06:20:11.936012","created_date":"2025-10-30T00:00:00"}