{"id":"https://openalex.org/W4394596924","doi":"https://doi.org/10.1109/csce60160.2023.00007","title":"Bootcamp Method for Training General Purpose AI Agents","display_name":"Bootcamp Method for Training General Purpose AI Agents","publication_year":2023,"publication_date":"2023-07-24","ids":{"openalex":"https://openalex.org/W4394596924","doi":"https://doi.org/10.1109/csce60160.2023.00007"},"language":"en","primary_location":{"id":"doi:10.1109/csce60160.2023.00007","doi":"https://doi.org/10.1109/csce60160.2023.00007","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/csce60160.2023.00007","pdf_url":null,"source":{"id":"https://openalex.org/S7407096130","display_name":"Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Vincent Lombardi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vincent Lombardi","raw_affiliation_strings":["School of EECS, Washington State University, Pullman, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of EECS, Washington State University, Pullman, WA, USA","institution_ids":["https://openalex.org/I72951846"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044712355","display_name":"Lawrence B. Holder","orcid":"https://orcid.org/0000-0002-6586-3144","observed_orcids":["https://orcid.org/0000-0002-6586-3144"]},"institutions":[{"id":"https://openalex.org/I72951846","display_name":"Washington State University","ror":"https://ror.org/05dk0ce17","country_code":"US","type":"education","lineage":["https://openalex.org/I72951846"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence Holder","raw_affiliation_strings":["School of EECS, Washington State University, Pullman, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of EECS, Washington State University, Pullman, WA, USA","institution_ids":["https://openalex.org/I72951846"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I72951846"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19865461,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9990000128746033,"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":0.9990000128746033,"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/T11574","display_name":"Artificial Intelligence in Games","score":0.0005000000237487257,"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/T10906","display_name":"AI-based Problem Solving and Planning","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"}}],"keywords":[{"id":"https://openalex.org/keywords/skill-acquisition","display_name":"skill acquisition","score":0.6919999718666077},{"id":"https://openalex.org/keywords/skill-selection","display_name":"skill selection","score":0.875},{"id":"https://openalex.org/keywords/vizdoom","display_name":"ViZDoom","score":0.8109999895095825},{"id":"https://openalex.org/keywords/simulation-environment","display_name":"simulation environment","score":0.5540000200271606},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"reinforcement learning","score":0.5099999904632568},{"id":"https://openalex.org/keywords/deep-q-network","display_name":"deep Q-network","score":0.7129999995231628}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6697496771812439},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5347843170166016},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41474097967147827},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/csce60160.2023.00007","doi":"https://doi.org/10.1109/csce60160.2023.00007","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/csce60160.2023.00007","pdf_url":null,"source":{"id":"https://openalex.org/S7407096130","display_name":"Congress in Computer Science, Computer Engineering, and Applied Computing (CSCE)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G6590411303","display_name":null,"funder_award_id":"W911NF\u201020\u20102\u20100004","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2120501001","https://openalex.org/W2144751739","https://openalex.org/W2260756217","https://openalex.org/W2362143032","https://openalex.org/W2591476770","https://openalex.org/W2736601468","https://openalex.org/W2751516180","https://openalex.org/W2903709781","https://openalex.org/W2914898814","https://openalex.org/W2920215304","https://openalex.org/W2964118262","https://openalex.org/W2965908679","https://openalex.org/W3158642699","https://openalex.org/W3164770271","https://openalex.org/W3190534097","https://openalex.org/W4280525617","https://openalex.org/W4283583520","https://openalex.org/W4295037249","https://openalex.org/W4309305985","https://openalex.org/W4313854613","https://openalex.org/W6741002519","https://openalex.org/W6838557027"],"related_works":["https://openalex.org/W7134048998","https://openalex.org/W4288055678","https://openalex.org/W7214950826","https://openalex.org/W7163696087","https://openalex.org/W2961587903","https://openalex.org/W7163363062","https://openalex.org/W1515621363","https://openalex.org/W4415886209","https://openalex.org/W7135346394","https://openalex.org/W4390873547"],"abstract_inverted_index":{"General":[0],"purpose":[1],"agents":[2,63,68,80,83,100],"have":[3],"long":[4],"been":[5],"an":[6,22,74],"ultimate":[7],"goal":[8,17],"of":[9,28,48],"AI":[10],"research.":[11],"One":[12],"promising":[13],"approach":[14],"to":[15,19,24,39,42,50],"this":[16],"is":[18],"first":[20],"train":[21],"agent":[23,38],"use":[25],"a":[26,31,58],"variety":[27],"skills,":[29],"called":[30,64],"skillnet":[32,62,82,99],"agent,":[33],"and":[34],"then":[35],"allow":[36],"the":[37,44,52,91],"learn":[40,70],"how":[41],"choose":[43,51],"appropriate":[45,53],"skill":[46],"instead":[47],"having":[49],"low-level":[54],"action.":[55],"We":[56,76,95],"propose":[57],"method":[59],"for":[60],"training":[61],"Bootcamp":[65,79],"that":[66,78,98],"helps":[67],"efficiently":[69],"basic":[71],"skills":[72],"in":[73,90,110],"environment.":[75,94],"found":[77,97],"outperform":[81,101],"trained":[84],"randomly":[85],"on":[86],"various":[87],"tasks":[88],"defined":[89],"ViZDoom":[92],"simulated":[93],"also":[96],"more":[102],"conventional":[103],"reinforcement-based":[104],"learning":[105],"approaches":[106],"such":[107],"as":[108],"DQNs":[109],"ViZDoom.":[111]},"counts_by_year":[],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2024-04-10T00:00:00"}