{"id":"https://openalex.org/W7140227396","doi":"https://doi.org/10.48550/arxiv.2603.21173","title":"Rethinking Plasticity in Deep Reinforcement Learning","display_name":"Rethinking Plasticity in Deep Reinforcement Learning","publication_year":2026,"publication_date":"2026-03-22","ids":{"openalex":"https://openalex.org/W7140227396","doi":"https://doi.org/10.48550/arxiv.2603.21173"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.21173","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21173","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.2603.21173","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"He, Zhiqiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"He, Zhiqiang","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":0.2231999933719635,"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":0.2231999933719635,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.14090000092983246,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.06889999657869339,"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/plasticity","display_name":"Plasticity","score":0.8065000176429749},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6276999711990356},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.529699981212616},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.47600001096725464},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46869999170303345},{"id":"https://openalex.org/keywords/equivalence","display_name":"Equivalence (formal languages)","score":0.4348999857902527}],"concepts":[{"id":"https://openalex.org/C79186407","wikidata":"https://www.wikidata.org/wiki/Q472074","display_name":"Plasticity","level":2,"score":0.8065000176429749},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6276999711990356},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.529699981212616},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5047000050544739},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.47600001096725464},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46869999170303345},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.4348999857902527},{"id":"https://openalex.org/C47611674","wikidata":"https://www.wikidata.org/wiki/Q849491","display_name":"Neuroplasticity","level":2,"score":0.38359999656677246},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38100001215934753},{"id":"https://openalex.org/C5687787","wikidata":"https://www.wikidata.org/wiki/Q5889850","display_name":"Homeostatic plasticity","level":5,"score":0.3587000072002411},{"id":"https://openalex.org/C141934464","wikidata":"https://www.wikidata.org/wiki/Q3305386","display_name":"Local optimum","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C159919123","wikidata":"https://www.wikidata.org/wiki/Q7577157","display_name":"Spike-timing-dependent plasticity","level":4,"score":0.28110000491142273},{"id":"https://openalex.org/C62277055","wikidata":"https://www.wikidata.org/wiki/Q295130","display_name":"Phenotypic plasticity","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.2506999969482422}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.21173","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21173","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.2603.21173","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.21173","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/1","display_name":"No poverty","score":0.7129085659980774}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"investigates":[2],"the":[3,47,53,89,99,105,145,154],"fundamental":[4],"mechanisms":[5],"driving":[6],"plasticity":[7,61,114,166,192],"loss":[8,62,115,167],"in":[9,125,172,193],"deep":[10,170],"reinforcement":[11],"learning":[12],"(RL),":[13],"a":[14,139,183],"critical":[15],"challenge":[16],"where":[17],"neural":[18],"networks":[19,120,135],"lose":[20],"their":[21],"ability":[22],"to":[23,25,45,138],"adapt":[24],"non-stationary":[26,178],"environments.":[27],"While":[28],"existing":[29],"research":[30],"often":[31],"relies":[32],"on":[33],"descriptive":[34],"metrics":[35],"like":[36],"dormant":[37],"neurons":[38],"or":[39],"effective":[40],"rank,":[41],"these":[42],"summaries":[43],"fail":[44],"explain":[46],"underlying":[48],"optimization":[49,156],"dynamics.":[50],"We":[51,86],"propose":[52],"Optimization-Centric":[54],"Plasticity":[55],"(OCP)":[56],"hypothesis,":[57],"which":[58],"posits":[59],"that":[60,98,113,144],"arises":[63],"because":[64],"optimal":[65],"points":[66],"from":[67],"previous":[68],"tasks":[69],"become":[70],"poor":[71],"local":[72,173],"optima":[73],"for":[74,187],"new":[75],"tasks,":[76],"trapping":[77],"parameters":[78],"during":[79],"task":[80,127],"transitions":[81],"and":[82,94,189],"hindering":[83],"subsequent":[84],"learning.":[85],"theoretically":[87],"establish":[88],"equivalence":[90],"between":[91],"neuron":[92],"dormancy":[93,123],"zero-gradient":[95],"states,":[96],"demonstrating":[97],"absence":[100],"of":[101,108],"gradient":[102],"signals":[103],"is":[104,116,151],"primary":[106],"driver":[107],"dormancy.":[109],"Our":[110],"experiments":[111],"reveal":[112],"highly":[117],"task-specific;":[118],"notably,":[119],"with":[121,132],"high":[122],"rates":[124],"one":[126],"can":[128],"achieve":[129],"performance":[130],"parity":[131],"randomly":[133],"initialized":[134],"when":[136],"switched":[137],"significantly":[140],"different":[141],"task,":[142],"suggesting":[143],"network's":[146],"capacity":[147],"remains":[148],"intact":[149],"but":[150],"inhibited":[152],"by":[153,168],"specific":[155],"landscape.":[157],"Furthermore,":[158],"our":[159,180],"hypothesis":[160],"elucidates":[161],"why":[162],"parameter":[163],"constraints":[164],"mitigate":[165],"preventing":[169],"entrenchment":[171],"optima.":[174],"Validated":[175],"across":[176],"diverse":[177],"scenarios,":[179],"findings":[181],"provide":[182],"rigorous":[184],"optimization-based":[185],"framework":[186],"understanding":[188],"restoring":[190],"network":[191],"complex":[194],"RL":[195],"domains.":[196]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-25T00:00:00"}