{"id":"https://openalex.org/W7133360024","doi":"https://doi.org/10.48550/arxiv.2603.01488","title":"LLM-assisted Semantic Option Discovery for Facilitating Adaptive Deep Reinforcement Learning","display_name":"LLM-assisted Semantic Option Discovery for Facilitating Adaptive Deep Reinforcement Learning","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133360024","doi":"https://doi.org/10.48550/arxiv.2603.01488"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.01488","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127941684","display_name":"Chang Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yao, Chang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091494473","display_name":"Jinghui Qin","orcid":"https://orcid.org/0000-0003-0663-199X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Jinghui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084382985","display_name":"Kebing Jin","orcid":"https://orcid.org/0000-0002-2788-5756"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Kebing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127997972","display_name":"Hankz Hankui Zhuo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhuo, Hankz Hankui","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5127941684"],"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":0.4350999891757965,"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":0.4350999891757965,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.11110000312328339,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.07079999893903732,"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/interpretability","display_name":"Interpretability","score":0.8831999897956848},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.7677000164985657},{"id":"https://openalex.org/keywords/executable","display_name":"Executable","score":0.6966999769210815},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5324000120162964},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.4918999969959259},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4339999854564667}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8831999897956848},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7677000164985657},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7572000026702881},{"id":"https://openalex.org/C160145156","wikidata":"https://www.wikidata.org/wiki/Q778586","display_name":"Executable","level":2,"score":0.6966999769210815},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5507000088691711},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5324000120162964},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.4918999969959259},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4339999854564667},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43050000071525574},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.31040000915527344},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3084000051021576},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C173404611","wikidata":"https://www.wikidata.org/wiki/Q528588","display_name":"Constraint programming","level":3,"score":0.27970001101493835},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.2711000144481659},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.266400009393692},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.25690001249313354},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.25220000743865967}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.01488","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.01488","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01488","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.01488","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Despite":[0],"achieving":[1],"remarkable":[2],"success":[3],"in":[4,17,68,157],"complex":[5],"tasks,":[6],"Deep":[7],"Reinforcement":[8],"Learning":[9],"(DRL)":[10],"is":[11,66],"still":[12],"suffering":[13],"from":[14],"critical":[15],"issues":[16],"practical":[18],"applications,":[19],"such":[20],"as":[21],"low":[22],"data":[23,158],"efficiency,":[24,159],"lack":[25],"of":[26,112,137],"interpretability,":[27],"and":[28,52,87,99,118,126,148,162],"limited":[29],"cross-environment":[30],"transferability.":[31,164],"However,":[32],"the":[33,44,109,135],"learned":[34],"policy":[35],"generating":[36],"actions":[37],"based":[38],"on":[39,143],"states":[40],"are":[41],"sensitive":[42],"to":[43,48,114,120],"environmental":[45],"changes,":[46],"struggling":[47],"guarantee":[49],"behavioral":[50],"safety":[51],"compliance.":[53],"Recent":[54],"research":[55],"shows":[56],"that":[57],"integrating":[58],"Large":[59],"Language":[60],"Models":[61],"(LLMs)":[62],"with":[63],"symbolic":[64],"planning":[65],"promising":[67],"addressing":[69],"these":[70],"challenges.":[71],"Inspired":[72],"by":[73,91],"this,":[74],"we":[75,140],"introduce":[76],"a":[77],"novel":[78],"LLM-driven":[79],"closed-loop":[80],"framework,":[81,139],"which":[82],"enables":[83],"semantic-driven":[84],"skill":[85],"reuse":[86],"real-time":[88],"constraint":[89,160],"monitoring":[90],"mapping":[92],"natural":[93],"language":[94],"instructions":[95],"into":[96],"executable":[97],"rules":[98],"semantically":[100],"annotating":[101],"automatically":[102],"created":[103],"options.":[104],"The":[105,152],"proposed":[106],"approach":[107],"utilizes":[108],"general":[110],"knowledge":[111],"LLMs":[113],"facilitate":[115],"exploration":[116],"efficiency":[117],"adapt":[119],"transferable":[121],"options":[122],"for":[123],"similar":[124],"environments,":[125],"provides":[127],"inherent":[128],"interpretability":[129],"through":[130],"semantic":[131],"annotations.":[132],"To":[133],"validate":[134],"effectiveness":[136],"this":[138],"conduct":[141],"experiments":[142],"two":[144],"domains,":[145],"Office":[146],"World":[147],"Montezuma's":[149],"Revenge,":[150],"respectively.":[151],"results":[153],"demonstrate":[154],"superior":[155],"performance":[156],"compliance,":[161],"cross-task":[163]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-03-04T00:00:00"}