{"id":"https://openalex.org/W7155357087","doi":"https://doi.org/10.48550/arxiv.2604.20627","title":"Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning","display_name":"Occupancy Reward Shaping: Improving Credit Assignment for Offline Goal-Conditioned Reinforcement Learning","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7155357087","doi":"https://doi.org/10.48550/arxiv.2604.20627"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.20627","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20627","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":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.2604.20627","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134393958","display_name":"Aravind Venugopal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Venugopal, Aravind","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134404997","display_name":"Jiayu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jiayu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134412751","display_name":"Xudong Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Xudong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134394353","display_name":"Chongyi Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Chongyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134364634","display_name":"Benjamin Eysenbach","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eysenbach, Benjamin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127850841","display_name":"Jeff Schneider","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schneider, Jeff","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":0.8657000064849854,"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":0.8657000064849854,"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/T10653","display_name":"Robot Manipulation and Learning","score":0.035999998450279236,"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"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.01360000018030405,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7204999923706055},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5649999976158142},{"id":"https://openalex.org/keywords/occupancy","display_name":"Occupancy","score":0.5476999878883362},{"id":"https://openalex.org/keywords/reset","display_name":"Reset (finance)","score":0.5339999794960022},{"id":"https://openalex.org/keywords/temporal-difference-learning","display_name":"Temporal difference learning","score":0.49050000309944153},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.47119998931884766},{"id":"https://openalex.org/keywords/lag","display_name":"Lag","score":0.44699999690055847},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.4262000024318695}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7204999923706055},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6470999717712402},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5649999976158142},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.5476999878883362},{"id":"https://openalex.org/C2779795794","wikidata":"https://www.wikidata.org/wiki/Q7315343","display_name":"Reset (finance)","level":2,"score":0.5339999794960022},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5205000042915344},{"id":"https://openalex.org/C196340769","wikidata":"https://www.wikidata.org/wiki/Q7698910","display_name":"Temporal difference learning","level":3,"score":0.49050000309944153},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.47119998931884766},{"id":"https://openalex.org/C75778745","wikidata":"https://www.wikidata.org/wiki/Q342626","display_name":"Lag","level":2,"score":0.44699999690055847},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4440000057220459},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.4262000024318695},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.36910000443458557},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2644999921321869},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C85044808","wikidata":"https://www.wikidata.org/wiki/Q620614","display_name":"Assignment problem","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C14646407","wikidata":"https://www.wikidata.org/wiki/Q1430750","display_name":"Bellman equation","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C25016198","wikidata":"https://www.wikidata.org/wiki/Q781833","display_name":"Temporal logic","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.20627","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20627","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":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.2604.20627","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.20627","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":null,"license_id":null,"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":{"The":[0],"temporal":[1,38,43,58],"lag":[2],"between":[3],"actions":[4],"and":[5,130],"their":[6],"long-term":[7],"consequences":[8],"makes":[9],"credit":[10,49,105],"assignment":[11,106],"a":[12,79,87],"challenge":[13],"when":[14],"learning":[15],"goal-directed":[16],"behaviors":[17],"from":[18,78],"data.":[19],"Generative":[20],"world":[21,62,143],"models":[22,63],"capture":[23],"the":[24,57,65,69,83,102,116,136,141],"distribution":[25],"of":[26,68,82,104,138],"future":[27],"states":[28],"an":[29],"agent":[30],"may":[31],"visit,":[32],"indicating":[33],"that":[34,42,90],"they":[35],"have":[36],"captured":[37],"information.":[39,93],"How":[40],"can":[41],"information":[44,59],"be":[45],"extracted":[46],"to":[47],"perform":[48],"assignment?":[50],"In":[51],"this":[52,76],"paper,":[53],"we":[54,74,134],"formalize":[55],"how":[56],"stored":[60],"in":[61,107,140],"encodes":[64],"underlying":[66],"geometry":[67,77],"world.":[70],"Leveraging":[71],"optimal":[72,117],"transport,":[73],"extract":[75],"learned":[80],"model":[81],"occupancy":[84],"measure":[85],"into":[86],"reward":[88,109],"function":[89],"captures":[91],"goal-reaching":[92],"Our":[94],"resulting":[95],"method,":[96],"Occupancy":[97],"Reward":[98],"Shaping,":[99],"largely":[100],"mitigates":[101],"problem":[103],"sparse":[108],"settings.":[110],"ORS":[111,139],"provably":[112],"does":[113],"not":[114],"alter":[115],"policy,":[118],"yet":[119],"empirically":[120],"improves":[121],"performance":[122],"by":[123],"2.2x":[124],"across":[125],"13":[126],"diverse":[127],"long-horizon":[128],"locomotion":[129],"manipulation":[131],"tasks.":[132,152],"Moreover,":[133],"demonstrate":[135],"effectiveness":[137],"real":[142],"for":[144],"controlling":[145],"nuclear":[146],"fusion":[147],"on":[148],"3":[149],"Tokamak":[150],"control":[151],"Code:":[153],"https://github.com/aravindvenu7/occupancy_reward_shaping;":[154],"Website:":[155],"https://aravindvenu7.github.io/website/ors/":[156]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-24T00:00:00"}
