{"id":"https://openalex.org/W6927130093","doi":"https://doi.org/10.26190/unsworks/23988","title":"Efficient Learning with Subgoals and Gaussian Process","display_name":"Efficient Learning with Subgoals and Gaussian Process","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W6927130093","doi":"https://doi.org/10.26190/unsworks/23988"},"language":"en","primary_location":{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/100281","is_oa":true,"landing_page_url":"http://hdl.handle.net/1959.4/100281","pdf_url":null,"source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/coar/resource_type/c_db06"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/1959.4/100281","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Jones, Ethan","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jones, Ethan","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":true,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.6492999792098999,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.6492999792098999,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.13940000534057617,"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":0.041099999099969864,"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/computation","display_name":"Computation","score":0.5770000219345093},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5394999980926514},{"id":"https://openalex.org/keywords/planner","display_name":"Planner","score":0.47850000858306885},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.47110000252723694},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.46380001306533813},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.46299999952316284},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.45590001344680786},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.39590001106262207}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6685000061988831},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5770000219345093},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5394999980926514},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.47850000858306885},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.47110000252723694},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.46299999952316284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.461899995803833},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.45590001344680786},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.39590001106262207},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.38769999146461487},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3580999970436096},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.27079999446868896},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.2581000030040741},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2522999942302704},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/100281","is_oa":true,"landing_page_url":"http://hdl.handle.net/1959.4/100281","pdf_url":null,"source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/coar/resource_type/c_db06"},{"id":"doi:10.26190/unsworks/23988","is_oa":true,"landing_page_url":"https://doi.org/10.26190/unsworks/23988","pdf_url":null,"source":{"id":"https://openalex.org/S7407053176","display_name":"University of New South Wales","issn_l":null,"issn":[],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dissertation"}],"best_oa_location":{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/100281","is_oa":true,"landing_page_url":"http://hdl.handle.net/1959.4/100281","pdf_url":null,"source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/coar/resource_type/c_db06"},"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":{"This":[0,37,198,238,255],"thesis":[1],"demonstrates":[2],"how":[3,190,205],"data":[4,34,73,172],"efficiency":[5,21,74],"in":[6,25,30,43,112,168,187,233],"reinforcement":[7,280],"learning":[8,176,281],"can":[9],"be":[10,40,216],"improved":[11,138],"through":[12,139],"the":[13,41,51,71,94,102,108,121,132,140,149,157,162,182,185,195,219,229,265],"use":[14,119,141,230,273],"of":[15,28,70,93,120,142,148,159,164,184,189,204,221,231,274],"subgoals":[16,144,232,246,275],"and":[17,244],"Gaussian":[18,88],"process.":[19],"Data":[20],"is":[22,35,75,137,178,199,236],"extremely":[23],"important":[24],"a":[26,56,59,66,257,261],"range":[27],"problems":[29,45],"which":[31],"gathering":[32],"additional":[33,213],"expensive.":[36],"tends":[38],"to":[39,106,110,125,130,145,170,180,201,270,276],"case":[42],"most":[44],"that":[46,84,223],"involve":[47],"actual":[48],"interactions":[49],"with":[50,218,250],"physical":[52],"world,":[53],"such":[54],"as":[55],"robot":[57],"kicking":[58],"ball,":[60],"an":[61,127,202,208,278],"autonomous":[62],"vehicle":[63],"driving":[64],"or":[65],"drone":[67],"manoeuvring.":[68],"State":[69],"art":[72],"achieved":[76],"on":[77],"several":[78],"well":[79],"researched":[80],"problems.":[81],"The":[82,96,115,134,152],"systems":[83],"achieve":[85],"this":[86],"learn":[87,107],"process":[89],"state":[90,103,122],"transition":[91,104,123],"models":[92],"problem.":[95,133],"model":[97,105,124],"based":[98],"learner":[99,155],"system":[100,177,262],"uses":[101],"action":[109],"take":[111],"each":[113],"state.":[114],"subgoal":[116,135,266],"planner":[117,136],"makes":[118],"build":[126],"explicit":[128],"plan":[129],"solve":[131],"learned":[143],"aid":[146],"navigation":[147],"problem":[150,234],"space.":[151],"resource":[153],"managed":[154],"balances":[156],"costs":[158,220],"computation":[160,214],"against":[161],"value":[163,183],"selecting":[165],"better":[166,207],"experiments":[167,186],"order":[169],"improve":[171,194],"efficiency.":[173],"An":[174],"active":[175],"used":[179],"estimate":[181,203],"terms":[188],"much":[191,206],"they":[192],"may":[193],"current":[196],"solution.":[197],"compared":[200],"experiment":[209],"found":[210],"by":[211],"expending":[212],"will":[215],"along":[217,249],"performing":[222],"computation.":[224],"A":[225],"theoretical":[226],"framework":[227,239,268],"around":[228],"solving":[235],"presented.":[237],"provides":[240],"insights":[241],"into":[242],"when":[243],"why":[245],"are":[247],"effective,":[248],"avenues":[251],"for":[252,260],"future":[253],"research.":[254],"includes":[256],"detailed":[258],"proposal":[259],"built":[263],"off":[264],"theory":[267],"intended":[269],"make":[271],"full":[272],"create":[277],"effective":[279],"system.":[282]},"counts_by_year":[],"updated_date":"2026-07-01T08:55:40.977307","created_date":"2025-10-10T00:00:00"}