{"id":"https://openalex.org/W7204115444","doi":"https://doi.org/10.48550/arxiv.2608.20799","title":"Dynamic Context Scheduling: Learning Beyond the Static Universe","display_name":"Dynamic Context Scheduling: Learning Beyond the Static Universe","publication_year":2026,"publication_date":"2026-08-21","ids":{"openalex":"https://openalex.org/W7204115444","doi":"https://doi.org/10.48550/arxiv.2608.20799"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2608.20799","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.20799","pdf_url":"https://arxiv.org/pdf/2608.20799","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.20799","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037689714","display_name":"Martin Mr\u00e1z","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mr\u00e1z, Martin","raw_affiliation_strings":["University of Freiburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Freiburg","institution_ids":["https://openalex.org/I161046081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059374472","display_name":"Andr\u00e9 Biedenkapp","orcid":"https://orcid.org/0000-0002-8703-8559","observed_orcids":["https://orcid.org/0000-0002-8703-8559"]},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Biedenkapp, Andr\u00e9","raw_affiliation_strings":["University of Freiburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Freiburg","institution_ids":["https://openalex.org/I161046081"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I161046081"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":1.0,"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":1.0,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":9.999999747378752e-05,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.0,"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/carl","display_name":"CARL","score":0.6570000052452087},{"id":"https://openalex.org/keywords/curriculum-learning","display_name":"curriculum learning","score":0.6850000023841858},{"id":"https://openalex.org/keywords/out-of-distribution-generalization","display_name":"out-of-distribution generalization","score":0.8930000066757202},{"id":"https://openalex.org/keywords/cosine-annealing","display_name":"cosine annealing","score":0.5830000042915344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7646999955177307},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.59170001745224},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5733000040054321},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5673999786376953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.512499988079071},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44369998574256897},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.4049000144004822},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.3901999890804291},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.38929998874664307},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.3336000144481659},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3158999979496002},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3156000077724457},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25780001282691956}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.20799","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.20799","pdf_url":"https://arxiv.org/pdf/2608.20799","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.20799","doi":"https://doi.org/10.48550/arxiv.2608.20799","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.20799","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,"listed_in":[],"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":"pmh:oai:arXiv.org:2608.20799","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.20799","pdf_url":"https://arxiv.org/pdf/2608.20799","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"display_name":"Quality education","id":"https://metadata.un.org/sdg/4","score":0.7457516193389893}],"study_designs":[],"awards":[{"id":"https://openalex.org/G1224374757","display_name":null,"funder_award_id":"35/1597\u20101","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G1244305882","display_name":null,"funder_award_id":"INST 35/1597-1","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G2628649996","display_name":null,"funder_award_id":"INST35/1597\u20101FUGG","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G4707917552","display_name":null,"funder_award_id":"35/1597-1 FUGG","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G5284521312","display_name":null,"funder_award_id":"INST 35","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204115444.pdf","grobid_xml":"https://content.openalex.org/works/W7204115444.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,61],"study":[1],"dynamic":[2,93],"context":[3,18,33,99],"scheduling":[4],"as":[5,20,27,75],"a":[6,21,28,41,49,64],"training":[7,37],"instrument":[8],"for":[9,107,129],"contextual":[10,68],"re-":[11],"inforcement":[12],"learning.":[13],"Rather":[14],"than":[15],"treating":[16],"intra-episode":[17],"variation":[19],"deployment":[22],"reality,":[23],"we":[24,90,115],"treat":[25],"it":[26],"controlled":[29],"shaping":[30],"mechanism.":[31],"Thereby,":[32],"evolves":[34],"within":[35],"each":[36],"episode":[38],"according":[39],"to":[40,48,141],"predetermined":[42],"schedule,":[43],"expos-":[44],"ing":[45],"the":[46,57,102,108],"policy":[47],"richer":[50],"and":[51,85,112],"more":[52,109],"temporally":[53],"structured":[54],"region":[55],"of":[56],"environment":[58],"parameter":[59],"space.":[60],"introduce":[62],"DYNAMICCARLENV,":[63],"framework":[65],"that":[66,92,126,136],"wraps":[67],"environments":[69,114],"with":[70,87],"pluggable":[71],"schedule":[72],"families,":[73],"such":[74],"sinusoidal":[76],"off-":[77],"sets":[78],"or":[79,96],"cosine":[80],"annealing.":[81],"Across":[82],"CartPole,":[83],"BipedalWalker":[84,111],"VehicleRacing":[86,113],"CARL":[88],"contextualization,":[89],"show":[91],"schedules":[94,135],"match":[95],"outperform":[97],"static":[98],"baselines":[100],"in":[101],"out-of-distribution":[103],"(OOD)":[104],"regimes.":[105],"Interestingly,":[106],"complex":[110],"also":[116],"achieve":[117],"higher":[118],"in-distribution":[119],"(ID)":[120],"evaluation":[121],"performance.":[122],"Preliminary":[123],"findings":[124],"indicate":[125],"automatic":[127],"search":[128,144],"multi-stage":[130],"curricula":[131],"can":[132],"successfully":[133],"discover":[134],"improve":[137],"generalization,":[138],"performing":[139],"comparably":[140],"extensive":[142],"grid":[143],"over":[145],"single-stage":[146],"schedulers.":[147]},"counts_by_year":[],"updated_date":"2026-09-30T09:27:27.532063","created_date":"2026-08-25T00:00:00"}