{"id":"https://openalex.org/W4402352023","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651127","title":"Effective State Space Exploration with Phase State Graph Generation and Goal-based Path Planning","display_name":"Effective State Space Exploration with Phase State Graph Generation and Goal-based Path Planning","publication_year":2024,"publication_date":"2024-06-30","ids":{"openalex":"https://openalex.org/W4402352023","doi":"https://doi.org/10.1109/ijcnn60899.2024.10651127"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn60899.2024.10651127","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651127","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 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/A5050894553","display_name":"Sinuo Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sinuo Zhang","raw_affiliation_strings":["School of Artificial Intelligence Jilin University,Changchun,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence Jilin University,Changchun,China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101847824","display_name":"Jifeng Hu","orcid":"https://orcid.org/0000-0002-5411-3073"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jifeng Hu","raw_affiliation_strings":["School of Artificial Intelligence Jilin University,Changchun,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence Jilin University,Changchun,China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004507415","display_name":"Xinqi Du","orcid":"https://orcid.org/0000-0003-0195-6859"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinqi Du","raw_affiliation_strings":["School of Artificial Intelligence Jilin University,Changchun,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence Jilin University,Changchun,China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036135101","display_name":"Zhejian Yang","orcid":"https://orcid.org/0000-0002-6980-8062"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhejian Yang","raw_affiliation_strings":["School of Artificial Intelligence Jilin University,Changchun,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence Jilin University,Changchun,China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101476708","display_name":"Yang Yu","orcid":"https://orcid.org/0000-0001-9592-8191"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yu","raw_affiliation_strings":["School of Artificial Intelligence Jilin University,Changchun,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence Jilin University,Changchun,China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108294333","display_name":"Hechang Chen","orcid":"https://orcid.org/0000-0001-7835-9556"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hechang Chen","raw_affiliation_strings":["School of Artificial Intelligence Jilin University,Changchun,China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence Jilin University,Changchun,China","institution_ids":["https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5050894553"],"corresponding_institution_ids":["https://openalex.org/I194450716"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14579856,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"1","last_page":"9"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9941999912261963,"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/T12288","display_name":"Optimization and Search Problems","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.6269301772117615},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.5528160333633423},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.545806348323822},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5455756187438965},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.5440903902053833},{"id":"https://openalex.org/keywords/any-angle-path-planning","display_name":"Any-angle path planning","score":0.4994199275970459},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.40250128507614136},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.35566672682762146},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.22970205545425415},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2176268994808197},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20546817779541016},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.11744394898414612},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.06790691614151001}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6269301772117615},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.5528160333633423},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.545806348323822},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5455756187438965},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.5440903902053833},{"id":"https://openalex.org/C158485040","wikidata":"https://www.wikidata.org/wiki/Q4778119","display_name":"Any-angle path planning","level":4,"score":0.4994199275970459},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.40250128507614136},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.35566672682762146},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.22970205545425415},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2176268994808197},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20546817779541016},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.11744394898414612},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.06790691614151001},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn60899.2024.10651127","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn60899.2024.10651127","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W169931978","https://openalex.org/W1969483458","https://openalex.org/W2145339207","https://openalex.org/W2227557434","https://openalex.org/W2736601468","https://openalex.org/W2787800669","https://openalex.org/W2885550588","https://openalex.org/W2888572441","https://openalex.org/W2963438456","https://openalex.org/W2995726179","https://openalex.org/W3016244212","https://openalex.org/W3033324992","https://openalex.org/W3037148280","https://openalex.org/W3089212412","https://openalex.org/W3089760622","https://openalex.org/W3102903061","https://openalex.org/W3168892396","https://openalex.org/W3174676553","https://openalex.org/W4226278401","https://openalex.org/W4285092328","https://openalex.org/W4290062243","https://openalex.org/W6684205842","https://openalex.org/W6741002519","https://openalex.org/W6747473740","https://openalex.org/W6748315105","https://openalex.org/W6748594472","https://openalex.org/W6748603076","https://openalex.org/W6753925943","https://openalex.org/W6760439459","https://openalex.org/W6764125455","https://openalex.org/W6771235960","https://openalex.org/W6779265984","https://openalex.org/W6784058722","https://openalex.org/W6796254985","https://openalex.org/W6796764164","https://openalex.org/W6798709053","https://openalex.org/W6810738896","https://openalex.org/W6841435136"],"related_works":["https://openalex.org/W2368795992","https://openalex.org/W4308080200","https://openalex.org/W2908094156","https://openalex.org/W2389896347","https://openalex.org/W2106119116","https://openalex.org/W4395071568","https://openalex.org/W2359600231","https://openalex.org/W1987886368","https://openalex.org/W2067790096","https://openalex.org/W1660309994"],"abstract_inverted_index":{"Exploring":[0],"the":[1,33,45,48,75,94,101,108,124,143],"state":[2,34,49,88],"space":[3,89,96,103],"efficiently":[4],"is":[5,114,130],"a":[6,41,67,86],"crucial":[7],"problem":[8],"in":[9,57,161],"reinforcement":[10,59],"learning":[11,18,25],"as":[12],"it":[13],"holds":[14],"significant":[15],"importance":[16],"for":[17,81,107,138],"optimal":[19],"policies.":[20],"One":[21],"effective":[22],"approach":[23],"involves":[24],"different":[26],"sub-policies":[27,118,126],"to":[28,40,54,92,116,132,145],"cover":[29],"various":[30,165],"sub-spaces":[31,98],"of":[32,47],"space,":[35],"with":[36],"each":[37],"sub-policy":[38],"corresponding":[39],"specific":[42],"goal.":[43],"However,":[44],"unevenness":[46],"probability":[50],"distribution":[51],"may":[52],"lead":[53],"exploration":[55,90],"difficulties":[56],"deep":[58],"learning.":[60],"To":[61],"overcome":[62],"this":[63],"challenge,":[64],"we":[65,84],"propose":[66],"Phase":[68],"State":[69],"Graph":[70],"Exploration":[71],"framework":[72,91],"(PSGE),":[73],"guiding":[74],"agent":[76,144,163],"towards":[77],"more":[78,147],"promising":[79],"directions":[80],"exploration.":[82],"Specifically,":[83],"design":[85],"graph-based":[87],"separate":[93],"combination":[95,102],"into":[97],"and":[99,104,119,127,153],"define":[100],"evaluation":[105],"criteria":[106],"agent\u2019s":[109,125],"sub-policies.":[110],"In":[111],"addition,":[112],"hypernetwork":[113],"leveraged":[115],"decouple":[117],"sub-goals,":[120],"ensuring":[121],"diversity":[122],"among":[123],"reward":[128,136],"shaping":[129],"used":[131],"provide":[133],"dense":[134],"internal":[135],"signals":[137],"policy":[139],"training,":[140],"which":[141],"encourages":[142],"learn":[146],"efficiently.":[148],"Experiments":[149],"on":[150],"combining":[151],"control":[152],"navigation":[154],"tasks":[155],"demonstrate":[156],"that":[157],"PSGE":[158],"performs":[159],"well":[160],"controlling":[162],"across":[164],"difficulty":[166],"level":[167],"tasks.":[168]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
