{"id":"https://openalex.org/W2477045463","doi":"https://doi.org/10.1109/acc.2016.7526516","title":"Receding-horizon planning using recursive Monte Carlo Tree Search with Sparse Action Sampling for continuous state and action spaces","display_name":"Receding-horizon planning using recursive Monte Carlo Tree Search with Sparse Action Sampling for continuous state and action spaces","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2477045463","doi":"https://doi.org/10.1109/acc.2016.7526516","mag":"2477045463"},"language":"en","primary_location":{"id":"doi:10.1109/acc.2016.7526516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acc.2016.7526516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 American Control Conference (ACC)","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/A5013396141","display_name":"Moritz Schneider","orcid":"https://orcid.org/0000-0003-1789-8593"},"institutions":[{"id":"https://openalex.org/I31512782","display_name":"Technische Universit\u00e4t Darmstadt","ror":"https://ror.org/05n911h24","country_code":"DE","type":"education","lineage":["https://openalex.org/I31512782"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Moritz Schneider","raw_affiliation_strings":["Laboratory of Control Methods and Robotics, Technische Universit\u00e4t Darmstadt, Darmstadt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Laboratory of Control Methods and Robotics, Technische Universit\u00e4t Darmstadt, Darmstadt, Germany","institution_ids":["https://openalex.org/I31512782"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5013396141"],"corresponding_institution_ids":["https://openalex.org/I31512782"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"12","issue":null,"first_page":"5401","last_page":"5406"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10791","display_name":"Advanced Control Systems Optimization","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T10791","display_name":"Advanced Control Systems Optimization","score":0.9865999817848206,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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":0.9811000227928162,"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":0.9577999711036682,"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/monte-carlo-tree-search","display_name":"Monte Carlo tree search","score":0.7909997701644897},{"id":"https://openalex.org/keywords/model-predictive-control","display_name":"Model predictive control","score":0.6403002738952637},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5819911360740662},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5605124831199646},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.544633686542511},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.5273716449737549},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5133863687515259},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.5073890089988708},{"id":"https://openalex.org/keywords/optimal-control","display_name":"Optimal control","score":0.5064195394515991},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.49417951703071594},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.49223557114601135},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.48665696382522583},{"id":"https://openalex.org/keywords/time-horizon","display_name":"Time horizon","score":0.4142928719520569},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3638594448566437},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2492971122264862},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.19413703680038452}],"concepts":[{"id":"https://openalex.org/C46149586","wikidata":"https://www.wikidata.org/wiki/Q11785332","display_name":"Monte Carlo tree search","level":3,"score":0.7909997701644897},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.6403002738952637},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5819911360740662},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5605124831199646},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.544633686542511},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.5273716449737549},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5133863687515259},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5073890089988708},{"id":"https://openalex.org/C91575142","wikidata":"https://www.wikidata.org/wiki/Q1971426","display_name":"Optimal control","level":2,"score":0.5064195394515991},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.49417951703071594},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.49223557114601135},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.48665696382522583},{"id":"https://openalex.org/C28761237","wikidata":"https://www.wikidata.org/wiki/Q7805321","display_name":"Time horizon","level":2,"score":0.4142928719520569},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3638594448566437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2492971122264862},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.19413703680038452},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acc.2016.7526516","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acc.2016.7526516","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 American Control Conference (ACC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6499999761581421,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W81137451","https://openalex.org/W107054272","https://openalex.org/W1825869920","https://openalex.org/W1972683690","https://openalex.org/W2108447966","https://openalex.org/W2120090487","https://openalex.org/W2126316555","https://openalex.org/W2161632769","https://openalex.org/W2399790246","https://openalex.org/W4285719527","https://openalex.org/W6603433805","https://openalex.org/W6676059824","https://openalex.org/W6712922004"],"related_works":["https://openalex.org/W2571592646","https://openalex.org/W1990079087","https://openalex.org/W4247855592","https://openalex.org/W2567165815","https://openalex.org/W2740304877","https://openalex.org/W3202234113","https://openalex.org/W2978000411","https://openalex.org/W2101188133","https://openalex.org/W4226164546","https://openalex.org/W2067790096"],"abstract_inverted_index":{"This":[0,113],"paper":[1],"introduces":[2],"a":[3,41,46,94],"recursive,":[4],"sampling-based":[5],"Monte":[6],"Carlo":[7],"Tree":[8],"Search":[9],"(MCTS)":[10],"approach":[11],"to":[12,105],"planning,":[13,62,68],"i.e.":[14],"receding":[15],"horizon":[16,49],"control,":[17],"in":[18,50,61,83,126],"continuous":[19,127],"state":[20,128],"and":[21,66,107,122,129],"action":[22,130],"nonlinear":[23,32,111,140],"set-point":[24],"control":[25,29,37,81,120],"problems.":[26],"Usually,":[27],"predictive":[28,142],"methods":[30],"for":[31,78,109],"systems":[33],"aim":[34],"at":[35],"(sub-)optimal":[36,79],"behavior":[38],"through":[39],"solving":[40],"dynamic":[42],"optimization":[43,71,96],"problem":[44,72],"over":[45],"fixed":[47],"prediction":[48],"every":[51],"time":[52],"step.":[53],"Tree-based":[54],"methods,":[55],"which":[56],"have":[57],"recently":[58],"gained":[59],"interest":[60],"model-based":[63],"reinforcement":[64],"learning":[65],"path":[67],"replace":[69],"the":[70,84,100,119,136],"by":[73],"an":[74],"incremental":[75],"probabilistic":[76],"search":[77],"open-loop":[80],"sequences":[82],"space":[85],"of":[86,92,124,138],"variable-length":[87],"closed-loop":[88],"trajectories.":[89],"The":[90],"benefit":[91],"using":[93],"weaker":[95],"procedure":[97],"is":[98,102,115],"that":[99],"algorithm":[101],"very":[103],"simple":[104],"understand/apply":[106],"works":[108],"general":[110],"systems.":[112],"article":[114],"concerned":[116],"with":[117],"increasing":[118],"performance":[121],"sampling-efficiency":[123],"MCTS":[125],"spaces":[131],"based":[132],"on":[133],"ideas":[134],"from":[135],"field":[137],"standard":[139],"model":[141],"control.":[143]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
