{"id":"https://openalex.org/W3043527030","doi":"https://doi.org/10.1162/isal_a_00299","title":"Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation","display_name":"Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3043527030","doi":"https://doi.org/10.1162/isal_a_00299","mag":"3043527030"},"language":"en","primary_location":{"id":"doi:10.1162/isal_a_00299","is_oa":true,"landing_page_url":"https://doi.org/10.1162/isal_a_00299","pdf_url":"https://www.mitpressjournals.org/doi/pdf/10.1162/isal_a_00299","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2020 Conference on Artificial Life","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mitpressjournals.org/doi/pdf/10.1162/isal_a_00299","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5033458962","display_name":"L\u00e9ni K. Le Goff","orcid":"https://orcid.org/0000-0003-1749-9154"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"L\u00e9ni K. Le Goff","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102803362","display_name":"Edgar Buchanan","orcid":"https://orcid.org/0000-0001-6587-8808"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Edgar Buchanan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006992974","display_name":"Emma Hart","orcid":"https://orcid.org/0000-0002-5405-4413"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emma Hart","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074492402","display_name":"A. E. Eiben","orcid":"https://orcid.org/0000-0002-3106-4213"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agoston E. Eiben","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057748433","display_name":"Wei Li","orcid":"https://orcid.org/0000-0001-8242-076X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083106608","display_name":"Matteo De Carlo","orcid":"https://orcid.org/0000-0002-6435-0873"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matteo de Carlo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014757139","display_name":"Matthew F. Hale","orcid":"https://orcid.org/0000-0003-1450-9193"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matthew F. Hale","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014169443","display_name":"Mike Angus","orcid":"https://orcid.org/0000-0002-6140-2623"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mike Angus","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086792904","display_name":"Robert Woolley","orcid":"https://orcid.org/0000-0002-7937-6582"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Robert Woolley","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080582720","display_name":"Jon Timmis","orcid":"https://orcid.org/0000-0003-1055-0471"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jon Timmis","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020850821","display_name":"Alan Winfield","orcid":"https://orcid.org/0000-0002-1476-3127"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alan Winfield","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5113452396","display_name":"Andrew M. Tyrrell","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrew M. Tyrrell","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":12,"corresponding_author_ids":["https://openalex.org/A5033458962"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.2535,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.90364775,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"432","last_page":"440"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9973000288009644,"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/T10100","display_name":"Metaheuristic Optimization Algorithms Research","score":0.9973000288009644,"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/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.996999979019165,"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":0.9959999918937683,"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/computer-science","display_name":"Computer science","score":0.6483752727508545},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.612755537033081},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5672227144241333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.506555438041687},{"id":"https://openalex.org/keywords/policy-learning","display_name":"Policy learning","score":0.4392915964126587},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4338243007659912}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6483752727508545},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.612755537033081},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5672227144241333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.506555438041687},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.4392915964126587},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4338243007659912},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1162/isal_a_00299","is_oa":true,"landing_page_url":"https://doi.org/10.1162/isal_a_00299","pdf_url":"https://www.mitpressjournals.org/doi/pdf/10.1162/isal_a_00299","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2020 Conference on Artificial Life","raw_type":"proceedings-article"},{"id":"pmh:oai:napier-surface.worktribe.com:2675888","is_oa":true,"landing_page_url":"https://napier-surface.worktribe.com/2675888/1/Sample%20And%20Time%20Efficient%20Policy%20Learning%20With%20CMA-ES%20And%20Bayesian%20Optimisation","pdf_url":null,"source":{"id":"https://openalex.org/S4306400544","display_name":"Research Output (Edinburgh Napier University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I251738","host_organization_name":"Edinburgh Napier University","host_organization_lineage":["https://openalex.org/I251738"],"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":"","raw_type":"Conference Proceeding"},{"id":"pmh:oai:repository@napier.ac.uk:2675888","is_oa":true,"landing_page_url":"http://researchrepository.napier.ac.uk/Output/2675888","pdf_url":null,"source":{"id":"https://openalex.org/S4306402591","display_name":"Edinburgh Napier Research Repository (Edinburgh Napier University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I251738","host_organization_name":"Edinburgh Napier University","host_organization_lineage":["https://openalex.org/I251738"],"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":"publishedVersion"}],"best_oa_location":{"id":"doi:10.1162/isal_a_00299","is_oa":true,"landing_page_url":"https://doi.org/10.1162/isal_a_00299","pdf_url":"https://www.mitpressjournals.org/doi/pdf/10.1162/isal_a_00299","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2020 Conference on Artificial Life","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3043527030.pdf","grobid_xml":"https://content.openalex.org/works/W3043527030.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4313488044","https://openalex.org/W3209574120","https://openalex.org/W3107602296","https://openalex.org/W4312192474","https://openalex.org/W4210805261"],"abstract_inverted_index":{"In":[0,69],"evolutionary":[1],"robot":[2,32],"systems":[3],"where":[4],"morphologies":[5],"and":[6,67,80,99,131,157],"controllers":[7],"of":[8,29,83,140],"real":[9],"robots":[10,124],"are":[11],"simultaneously":[12],"evolved,":[13],"it":[14,38],"is":[15,19],"clear":[16],"that":[17,135],"there":[18],"likely":[20],"to":[21,24,35,39,53,76,143],"be":[22,46,64],"requirements":[23],"refine":[25],"the":[26,78,84,127,138,147,151,161],"inherited":[27],"controller":[28],"a":[30,49,61],"'newborn'":[31],"in":[33],"order":[34],"better":[36,155],"align":[37],"its":[40,118],"newly":[41],"generated":[42],"morphology.":[43],"This":[44],"can":[45],"accomplished":[47],"via":[48],"learning":[50],"mechanism":[51,62,104],"applied":[52],"each":[54],"individual":[55],"robot:":[56],"for":[57,117],"practical":[58],"reasons,":[59],"such":[60],"should":[63],"both":[65],"sample":[66,79,119,156],"time-efficient.":[68],"this":[70],"paper,":[71],"We":[72],"investigate":[73],"two":[74,123],"ways":[75],"improve":[77],"time":[81,158],"efficiency":[82,159],"well-known":[85],"learner":[86],"CMA-ES":[87,95,112],"on":[88],"navigation":[89],"tasks.":[90],"The":[91,109],"first":[92],"approach":[93],"combines":[94],"with":[96,105,126],"Novelty":[97],"Search,":[98],"includes":[100],"an":[101],"adaptive":[102],"restart":[103,149],"increasing":[106],"population":[107],"size.":[108],"second":[110],"bootstraps":[111],"using":[113,122],"Bayesian":[114],"Optimisation,":[115],"known":[116],"efficiency.":[120],"Results":[121],"built":[125],"ARE":[128],"project's":[129],"modules":[130],"four":[132],"environments":[133],"show":[134],"novelty":[136],"reduces":[137],"number":[139],"samples":[141],"needed":[142],"converge,":[144],"as":[145],"does":[146],"custom":[148],"mechanism;":[150],"latter":[152],"also":[153],"has":[154],"than":[160],"hybridised":[162],"Bayesian/Evolutionary":[163],"method.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}