{"id":"https://openalex.org/W4320342021","doi":"https://doi.org/10.48550/arxiv.2302.01301","title":"MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks","display_name":"MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks","publication_year":2023,"publication_date":"2023-02-02","ids":{"openalex":"https://openalex.org/W4320342021","doi":"https://doi.org/10.48550/arxiv.2302.01301"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2302.01301","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.01301","pdf_url":"https://arxiv.org/pdf/2302.01301","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2302.01301","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059453270","display_name":"Raffaele Galliera","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Galliera, Raffaele","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065552686","display_name":"Alessandro Morelli","orcid":"https://orcid.org/0000-0003-3309-3461"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Morelli, Alessandro","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063993549","display_name":"Roberto Fronteddu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fronteddu, Roberto","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5047412092","display_name":"Niranjan Suri","orcid":"https://orcid.org/0000-0003-2982-8218"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Suri, Niranjan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5059453270"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.6438999772071838,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.6438999772071838,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.5609999895095825,"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/T10558","display_name":"Advancements in Semiconductor Devices and Circuit Design","score":0.5403000116348267,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8572948575019836},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7227485775947571},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.6980390548706055},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5375608205795288},{"id":"https://openalex.org/keywords/network-congestion","display_name":"Network congestion","score":0.4896314740180969},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.4401533901691437},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4085187017917633},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.40299975872039795},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4020816385746002},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.13272437453269958},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13044381141662598},{"id":"https://openalex.org/keywords/systems-engineering","display_name":"Systems engineering","score":0.08940964937210083}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8572948575019836},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7227485775947571},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.6980390548706055},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5375608205795288},{"id":"https://openalex.org/C195563490","wikidata":"https://www.wikidata.org/wiki/Q180368","display_name":"Network congestion","level":3,"score":0.4896314740180969},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.4401533901691437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4085187017917633},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.40299975872039795},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4020816385746002},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.13272437453269958},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13044381141662598},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.08940964937210083},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2302.01301","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.01301","pdf_url":"https://arxiv.org/pdf/2302.01301","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2302.01301","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2302.01301","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2302.01301","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2302.01301","pdf_url":"https://arxiv.org/pdf/2302.01301","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4320342021.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2140186469","https://openalex.org/W4390421286","https://openalex.org/W4280563792","https://openalex.org/W4389724018","https://openalex.org/W4318719684","https://openalex.org/W3183136280","https://openalex.org/W4318559728","https://openalex.org/W2775233965","https://openalex.org/W4281847915","https://openalex.org/W4312713068"],"abstract_inverted_index":{"Fast":[0],"and":[1,25,34,101,103,148],"efficient":[2],"transport":[3],"protocols":[4],"are":[5],"the":[6,29,39,93,105,126,143,200],"foundation":[7],"of":[8,15,32,41,54,67,145],"an":[9,71,109],"increasingly":[10],"distributed":[11],"world.":[12],"The":[13,52],"burden":[14],"continuously":[16],"delivering":[17],"improved":[18],"communication":[19],"performance":[20],"to":[21,63,81,97,124,136,151,168],"support":[22],"next-generation":[23],"applications":[24],"services,":[26],"combined":[27],"with":[28,119,170],"increasing":[30],"heterogeneity":[31],"systems":[33],"network":[35,118],"technologies,":[36],"has":[37],"promoted":[38],"design":[40],"Congestion":[42],"Control":[43],"(CC)":[44],"algorithms":[45,197],"that":[46,60,129,162,186],"perform":[47],"well":[48],"under":[49],"specific":[50],"environments.":[51],"challenge":[53],"designing":[55],"a":[56,64,83,116,175,190],"generic":[57],"CC":[58,196],"algorithm":[59,96],"can":[61,164],"adapt":[62],"broad":[65],"range":[66],"scenarios":[68],"is":[69,157],"still":[70],"open":[72],"research":[73,156],"question.":[74],"To":[75],"tackle":[76],"this":[77],"challenge,":[78],"we":[79,184],"propose":[80],"apply":[82],"novel":[84],"Reinforcement":[85],"Learning":[86],"(RL)":[87],"approach.":[88],"Our":[89],"solution,":[90],"MARLIN,":[91],"uses":[92],"Soft":[94],"Actor-Critic":[95],"maximize":[98],"both":[99],"entropy":[100,202],"return":[102],"models":[104],"learning":[106],"process":[107],"as":[108],"infinite-horizon":[110],"task.":[111],"We":[112,138],"trained":[113],"MARLIN":[114,163],"on":[115,142,199],"real":[117],"varying":[120],"background":[121],"traffic":[122],"patterns":[123],"overcome":[125],"sim-to-real":[127],"mismatch":[128],"researchers":[130],"have":[131,160],"encountered":[132],"when":[133],"applying":[134],"RL":[135,203],"CC.":[137],"evaluated":[139],"our":[140,187],"solution":[141],"task":[144,176],"file":[146],"transfer":[147],"compared":[149],"it":[150],"TCP":[152,169],"Cubic.":[153],"While":[154],"further":[155],"required,":[158],"results":[159,167],"shown":[161],"achieve":[165],"comparable":[166],"little":[171],"hyperparameter":[172],"tuning,":[173],"in":[174],"significantly":[177],"different":[178],"from":[179],"its":[180],"training":[181],"setting.":[182],"Therefore,":[183],"believe":[185],"work":[188],"represents":[189],"promising":[191],"first":[192],"step":[193],"toward":[194],"building":[195],"based":[198],"maximum":[201],"framework.":[204]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}