{"id":"https://openalex.org/W3090490555","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206918","title":"Monoceros: A New Approach for Training an Agent to Play FPS Games","display_name":"Monoceros: A New Approach for Training an Agent to Play FPS Games","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3090490555","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206918","mag":"3090490555"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","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":"https://openalex.org/A5009039982","display_name":"Ruiyang Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210128818","display_name":"Institute of Software","ror":"https://ror.org/033dfsn42","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128818"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruiyang Yang","raw_affiliation_strings":["State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128818","https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109377270","display_name":"Hongyin Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]},{"id":"https://openalex.org/I4210128818","display_name":"Institute of Software","ror":"https://ror.org/033dfsn42","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128818"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyin Tang","raw_affiliation_strings":["State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128818","https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066621776","display_name":"Beihong Jin","orcid":"https://orcid.org/0000-0003-3683-4034"},"institutions":[{"id":"https://openalex.org/I4210128818","display_name":"Institute of Software","ror":"https://ror.org/033dfsn42","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128818"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Beihong Jin","raw_affiliation_strings":["State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Computer Sciences, Institute of Software, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210128818","https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5009039982"],"corresponding_institution_ids":["https://openalex.org/I4210128818","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11848549,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"3","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":0.9987000226974487,"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":0.9987000226974487,"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":0.9950000047683716,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8439459800720215},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7300950288772583},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5828516483306885},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5672762989997864},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.4898512065410614},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.48870375752449036},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.48451822996139526},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4738120138645172},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.41770148277282715},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3770322799682617},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.06207355856895447}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8439459800720215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7300950288772583},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5828516483306885},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5672762989997864},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.4898512065410614},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.48870375752449036},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.48451822996139526},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4738120138645172},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.41770148277282715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3770322799682617},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.06207355856895447},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"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/ijcnn48605.2020.9206918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1757796397","https://openalex.org/W1771410628","https://openalex.org/W1999874108","https://openalex.org/W2051228319","https://openalex.org/W2098774185","https://openalex.org/W2099471712","https://openalex.org/W2121092017","https://openalex.org/W2145339207","https://openalex.org/W2466175722","https://openalex.org/W2522489477","https://openalex.org/W2580175322","https://openalex.org/W2604268026","https://openalex.org/W2736601468","https://openalex.org/W2751516180","https://openalex.org/W2899205164","https://openalex.org/W2945023859","https://openalex.org/W2950102663","https://openalex.org/W2962747693","https://openalex.org/W2962938178","https://openalex.org/W2963277051","https://openalex.org/W2963871073","https://openalex.org/W2964043796","https://openalex.org/W2964067469","https://openalex.org/W3103559770","https://openalex.org/W3123212791","https://openalex.org/W4298857966","https://openalex.org/W4320013936","https://openalex.org/W6637967152","https://openalex.org/W6638018090","https://openalex.org/W6674884181","https://openalex.org/W6677939520","https://openalex.org/W6692846177","https://openalex.org/W6718092244","https://openalex.org/W6727252785","https://openalex.org/W6732665253","https://openalex.org/W6736309856","https://openalex.org/W6741002519","https://openalex.org/W6743660412","https://openalex.org/W6756303580","https://openalex.org/W6762387550"],"related_works":["https://openalex.org/W4387497383","https://openalex.org/W3183948672","https://openalex.org/W3173606202","https://openalex.org/W3110381201","https://openalex.org/W2948807893","https://openalex.org/W2935909890","https://openalex.org/W2778153218","https://openalex.org/W2758277628","https://openalex.org/W1531601525","https://openalex.org/W2665305151"],"abstract_inverted_index":{"In":[0,30],"the":[1,5,11,24,48,56,61,74,94,100,109,119,136,145,147,153,165,170],"deep":[2,166],"reinforcement":[3,167],"learning,":[4,172],"sparse":[6,57],"reward":[7,58,69,82],"problem":[8,59],"directly":[9],"impacts":[10],"quality":[12],"of":[13],"agent":[14,62,148],"training.":[15,63],"Existing":[16],"methods":[17,163],"have":[18],"not":[19],"been":[20],"satisfactory,":[21],"especially":[22],"for":[23,47],"scenarios":[25,49,134],"with":[26,50],"high-dimensional":[27,51,95],"state":[28,52],"information.":[29],"this":[31],"paper,":[32],"we":[33,65,85],"propose":[34],"a":[35,41,67,87,104],"new":[36],"approach":[37],"Monoceros":[38,44,113,151],"to":[39,91,93,107,117,121],"training":[40,110],"game":[42],"agent.":[43],"can":[45,114],"work":[46],"information":[53,96],"and":[54,80,98,158,169],"alleviate":[55],"during":[60],"Specifically,":[64],"present":[66],"composite":[68],"function":[70],"which":[71,160],"combines":[72],"both":[73],"knowledge":[75],"implied":[76],"in":[77,135,143,164],"expert":[78],"trajectories":[79],"manually-set":[81],"functions.":[83],"Moreover,":[84],"design":[86],"specific":[88],"policy":[89],"network":[90],"adapt":[92],"scenarios,":[97,146],"adopt":[99],"behavior":[101],"clone":[102],"as":[103],"pre-training":[105],"strategy":[106],"accelerate":[108],"process.":[111],"Technically,":[112],"be":[115],"applied":[116],"train":[118],"agents":[120,154],"play":[122],"First":[123],"Person":[124],"Shooter":[125],"(FPS)":[126],"games.":[127],"We":[128],"conduct":[129],"extensive":[130],"experiments":[131],"on":[132],"three":[133],"VIZDoom":[137],"platform.":[138],"Experimental":[139],"results":[140],"show":[141],"that":[142],"all":[144],"trained":[149,155],"by":[150,156],"outperforms":[152],"Arnold":[157],"GAIL,":[159],"are":[161],"representative":[162],"learning":[168],"imitation":[171],"respectively.":[173]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}