{"id":"https://openalex.org/W4212849244","doi":"https://doi.org/10.1109/asp-dac52403.2022.9712561","title":"Neural Network Pruning and Fast Training for DRL-based UAV Trajectory Planning","display_name":"Neural Network Pruning and Fast Training for DRL-based UAV Trajectory Planning","publication_year":2022,"publication_date":"2022-01-17","ids":{"openalex":"https://openalex.org/W4212849244","doi":"https://doi.org/10.1109/asp-dac52403.2022.9712561"},"language":"en","primary_location":{"id":"doi:10.1109/asp-dac52403.2022.9712561","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asp-dac52403.2022.9712561","pdf_url":null,"source":{"id":"https://openalex.org/S4363608500","display_name":"2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5102936390","display_name":"Yilan Li","orcid":"https://orcid.org/0000-0002-4157-7792"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yilan Li","raw_affiliation_strings":["Syracuse University,Department of Electrical Engineering & Computer Science,Syracuse,NY,USA,13244"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University,Department of Electrical Engineering & Computer Science,Syracuse,NY,USA,13244","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112473990","display_name":"Haowen Fang","orcid":"https://orcid.org/0009-0009-7551-3373"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haowen Fang","raw_affiliation_strings":["Syracuse University,Department of Electrical Engineering & Computer Science,Syracuse,NY,USA,13244"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University,Department of Electrical Engineering & Computer Science,Syracuse,NY,USA,13244","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100443445","display_name":"Mingyang Li","orcid":"https://orcid.org/0000-0003-4410-0292"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingyang Li","raw_affiliation_strings":["Syracuse University,Department of Electrical Engineering & Computer Science,Syracuse,NY,USA,13244"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University,Department of Electrical Engineering & Computer Science,Syracuse,NY,USA,13244","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108378143","display_name":"Yue Ma","orcid":"https://orcid.org/0009-0000-5485-4237"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yue Ma","raw_affiliation_strings":["Syracuse University,Department of Electrical Engineering & Computer Science,Syracuse,NY,USA,13244"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University,Department of Electrical Engineering & Computer Science,Syracuse,NY,USA,13244","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018468480","display_name":"Qinru Qiu","orcid":"https://orcid.org/0000-0003-2546-0655"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Qinru Qiu","raw_affiliation_strings":["Syracuse University,Department of Electrical Engineering & Computer Science,Syracuse,NY,USA,13244"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University,Department of Electrical Engineering & Computer Science,Syracuse,NY,USA,13244","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I70983195"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"574","last_page":"579"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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":0.9994999766349792,"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":0.9994999766349792,"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/pruning","display_name":"Pruning","score":0.8096441030502319},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7577906847000122},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6449187994003296},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6418067812919617},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6238502860069275},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5274940729141235},{"id":"https://openalex.org/keywords/payload","display_name":"Payload (computing)","score":0.5044897794723511},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.4964762330055237},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4616175591945648},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.44951462745666504},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.43971022963523865},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4269624352455139},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10640570521354675},{"id":"https://openalex.org/keywords/network-packet","display_name":"Network packet","score":0.0671902596950531}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8096441030502319},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7577906847000122},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6449187994003296},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6418067812919617},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6238502860069275},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5274940729141235},{"id":"https://openalex.org/C134066672","wikidata":"https://www.wikidata.org/wiki/Q1424639","display_name":"Payload (computing)","level":3,"score":0.5044897794723511},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.4964762330055237},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4616175591945648},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.44951462745666504},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.43971022963523865},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4269624352455139},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10640570521354675},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.0671902596950531},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"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/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asp-dac52403.2022.9712561","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asp-dac52403.2022.9712561","pdf_url":null,"source":{"id":"https://openalex.org/S4363608500","display_name":"2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.800000011920929}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1977655452","https://openalex.org/W2145339207","https://openalex.org/W2164278908","https://openalex.org/W2554302513","https://openalex.org/W2884180697","https://openalex.org/W2940644440","https://openalex.org/W2948635472","https://openalex.org/W2952088488","https://openalex.org/W2967094746","https://openalex.org/W2968430912","https://openalex.org/W2987705663","https://openalex.org/W6732814185","https://openalex.org/W6753929555","https://openalex.org/W6763726227"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2027972911","https://openalex.org/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W2111089054"],"abstract_inverted_index":{"Deep":[0],"reinforcement":[1],"learning":[2],"(DRL)":[3],"has":[4,37],"been":[5],"applied":[6],"for":[7,47,57,182],"optimal":[8],"control":[9],"of":[10,20,30,83,132,144,163,178],"autonomous":[11],"UAV":[12],"trajectory":[13,168],"generation.":[14],"The":[15,103,124],"energy":[16],"and":[17,28,71,86,91,108,158,166],"payload":[18],"capacity":[19],"small":[21],"UAVs":[22],"impose":[23],"constraints":[24],"on":[25,50,79,117],"the":[26,31,38,42,64,68,81,121,137,142,145,155,164,179,183],"complexity":[27],"size":[29],"neural":[32,44,55],"network.":[33],"While":[34],"Model":[35],"compression":[36],"potential":[39],"to":[40,63,119],"optimize":[41],"trained":[43],"network":[45,56],"model":[46],"efficient":[48],"deployment":[49],"em-bedded":[51],"platforms,":[52],"pruning":[53,107,125,159],"a":[54,175,186],"DRL":[58,84,109],"is":[59,111],"more":[60],"difficult":[61],"due":[62],"slow":[65],"convergence":[66,99],"in":[67,98],"training":[69,85,110,122,138,157],"before":[70],"after":[72],"pruning.":[73,87],"In":[74],"this":[75],"work,":[76],"we":[77],"focus":[78],"improving":[80],"speed":[82],"New":[88],"reward":[89],"function":[90],"action":[92],"exploration":[93],"are":[94,150],"first":[95],"introduced,":[96],"resulting":[97,185],"speedup":[100],"by":[101,171],"34.14%.":[102],"framework":[104,160],"that":[105,154],"integrates":[106],"then":[112],"presented":[113],"with":[114],"an":[115],"emphasize":[116],"how":[118],"reduce":[120],"cost.":[123],"does":[126],"not":[127],"only":[128],"improve":[129],"computational":[130],"performance":[131],"inference,":[133,184],"but":[134],"also":[135],"reduces":[136,161],"effort":[139],"with-out":[140],"compromising":[141],"quality":[143],"trajectory.":[146],"Finally,":[147],"experimental":[148],"results":[149],"presented.":[151],"We":[152],"show":[153],"integrated":[156],"67.16%":[162],"weight":[165],"improves":[167],"success":[169],"rate":[170],"1.7%.":[172],"It":[173],"achieves":[174],"4.43x":[176],"reduction":[177],"floating-point":[180],"operations":[181],"measured":[187],"41.85%":[188],"run":[189],"time":[190],"reduction.":[191]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}