{"id":"https://openalex.org/W2775268899","doi":"https://doi.org/10.1109/smc.2017.8122877","title":"Effective lazy training method for deep q-network in obstacle avoidance and path planning","display_name":"Effective lazy training method for deep q-network in obstacle avoidance and path planning","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2775268899","doi":"https://doi.org/10.1109/smc.2017.8122877","mag":"2775268899"},"language":"en","primary_location":{"id":"doi:10.1109/smc.2017.8122877","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2017.8122877","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","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/A5100753756","display_name":"Juan Wu","orcid":"https://orcid.org/0000-0002-7241-2771"},"institutions":[{"id":"https://openalex.org/I129672417","display_name":"Namseoul University","ror":"https://ror.org/0509ndt57","country_code":"KR","type":"education","lineage":["https://openalex.org/I129672417"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Juan Wu","raw_affiliation_strings":["Department of Computer Science, Namseoul University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Namseoul University, Seoul, Korea","institution_ids":["https://openalex.org/I129672417"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074123443","display_name":"Seabyuk Shin","orcid":null},"institutions":[{"id":"https://openalex.org/I129672417","display_name":"Namseoul University","ror":"https://ror.org/0509ndt57","country_code":"KR","type":"education","lineage":["https://openalex.org/I129672417"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Seabyuk Shin","raw_affiliation_strings":["Department of Computer Science, Namseoul University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Namseoul University, Seoul, Korea","institution_ids":["https://openalex.org/I129672417"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039059145","display_name":"Cheong-Gil Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I129672417","display_name":"Namseoul University","ror":"https://ror.org/0509ndt57","country_code":"KR","type":"education","lineage":["https://openalex.org/I129672417"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Cheong-Gil Kim","raw_affiliation_strings":["Department of Computer Science, Namseoul University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Namseoul University, Seoul, Korea","institution_ids":["https://openalex.org/I129672417"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032646530","display_name":"Shin\u2010Dug Kim","orcid":"https://orcid.org/0000-0002-2642-6662"},"institutions":[{"id":"https://openalex.org/I129672417","display_name":"Namseoul University","ror":"https://ror.org/0509ndt57","country_code":"KR","type":"education","lineage":["https://openalex.org/I129672417"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Shin-Dug Kim","raw_affiliation_strings":["Department of Computer Science, Namseoul University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Namseoul University, Seoul, Korea","institution_ids":["https://openalex.org/I129672417"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100753756"],"corresponding_institution_ids":["https://openalex.org/I129672417"],"apc_list":null,"apc_paid":null,"fwci":2.1061,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.88974882,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1799","last_page":"1804"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9987999796867371,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.993399977684021,"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":0.8445547819137573},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8203408718109131},{"id":"https://openalex.org/keywords/obstacle-avoidance","display_name":"Obstacle avoidance","score":0.8029754757881165},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.6910609602928162},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6691153049468994},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.6391832828521729},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.6354029774665833},{"id":"https://openalex.org/keywords/q-learning","display_name":"Q-learning","score":0.6035172939300537},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5970064401626587},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.5832198262214661},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5558100938796997},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4556950628757477},{"id":"https://openalex.org/keywords/shortest-path-problem","display_name":"Shortest path problem","score":0.44255462288856506},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41666197776794434},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.27759766578674316},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.12367266416549683},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.08983194828033447},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.0767604410648346},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.06683158874511719},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.05736786127090454}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8445547819137573},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8203408718109131},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.8029754757881165},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.6910609602928162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6691153049468994},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.6391832828521729},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.6354029774665833},{"id":"https://openalex.org/C188116033","wikidata":"https://www.wikidata.org/wiki/Q2664563","display_name":"Q-learning","level":3,"score":0.6035172939300537},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5970064401626587},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.5832198262214661},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5558100938796997},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4556950628757477},{"id":"https://openalex.org/C22590252","wikidata":"https://www.wikidata.org/wiki/Q1058754","display_name":"Shortest path problem","level":3,"score":0.44255462288856506},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41666197776794434},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.27759766578674316},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.12367266416549683},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.08983194828033447},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0767604410648346},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.06683158874511719},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.05736786127090454},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/smc.2017.8122877","is_oa":false,"landing_page_url":"https://doi.org/10.1109/smc.2017.8122877","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1757796397","https://openalex.org/W2118688707","https://openalex.org/W2121863487","https://openalex.org/W2145339207","https://openalex.org/W2562788852","https://openalex.org/W2570651606","https://openalex.org/W2575705757","https://openalex.org/W2582616844","https://openalex.org/W2583362313","https://openalex.org/W2962841471","https://openalex.org/W4214717370","https://openalex.org/W4298857966","https://openalex.org/W6637967152","https://openalex.org/W6677477928","https://openalex.org/W6732386917"],"related_works":["https://openalex.org/W2913749762","https://openalex.org/W2531604492","https://openalex.org/W2050548713","https://openalex.org/W4380590094","https://openalex.org/W2140606111","https://openalex.org/W2991662304","https://openalex.org/W3119736302","https://openalex.org/W4381746183","https://openalex.org/W3200041777","https://openalex.org/W2794689129"],"abstract_inverted_index":{"Deep":[0],"reinforcement":[1,5,25],"learning":[2,6,60],"technique":[3],"combines":[4],"and":[7,43,62,98,152],"neural":[8,32],"network":[9,33],"for":[10,23,28,39,94],"various":[11],"applications.":[12,46],"This":[13],"paper":[14],"is":[15,92,110],"to":[16,36,112,118,144],"propose":[17],"an":[18,103,107],"effective":[19],"lazy":[20,59],"training":[21,54,70,139,155],"method":[22,49,61,64,91,135,147],"deep":[24,29],"learning,":[26],"especially":[27],"Q-network":[30],"combining":[31],"with":[34,148],"Q-learning":[35],"be":[37],"used":[38],"the":[40,52,69,95,115,119,128,133,145,154],"obstacle":[41,96],"avoidance":[42,97],"path":[44,99,117],"planning":[45,100],"The":[47,89],"proposed":[48,90,134],"can":[50,75],"reduce":[51,76],"overall":[53],"time":[55,83,140],"by":[56],"designing":[57],"a":[58,63,77],"removing":[65],"unnecessary":[66],"repetitions":[67],"in":[68,106],"step.":[71],"These":[72],"two":[73],"methods":[74],"significant":[78],"portion":[79],"of":[80,138],"total":[81],"execution":[82],"without":[84,121],"losing":[85],"any":[86,122],"required":[87],"accuracy.":[88],"evaluated":[93],"tasks,":[101],"where":[102],"agent":[104],"trapped":[105],"unknown":[108],"environment":[109],"trying":[111],"find":[113],"out":[114],"shortest":[116],"destination":[120],"collision,":[123],"through":[124],"its":[125],"self-study.":[126],"And":[127],"experiment":[129],"results":[130],"show":[131],"that":[132],"reduces":[136],"53.38%":[137],"on":[141],"average,":[142],"compared":[143],"traditional":[146],"no":[149],"performance":[150],"loss":[151],"make":[153],"procedure":[156],"more":[157],"stable.":[158]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}