{"id":"https://openalex.org/W7166063930","doi":"https://doi.org/10.48550/arxiv.2606.26168","title":"Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration","display_name":"Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration","publication_year":2026,"publication_date":"2026-06-24","ids":{"openalex":"https://openalex.org/W7166063930","doi":"https://doi.org/10.48550/arxiv.2606.26168"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.26168","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26168","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.26168","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133071321","display_name":"Ruyi Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210151430","display_name":"Institute of Aging","ror":"https://ror.org/044mbfr67","country_code":"CA","type":"facility","lineage":["https://openalex.org/I176337269","https://openalex.org/I4210151430"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Tang, Ruyi","raw_affiliation_strings":["LCQB-AG"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LCQB-AG","institution_ids":["https://openalex.org/I4210151430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083609970","display_name":"Gr\u00e9goire Sergeant-Perthuis","orcid":"https://orcid.org/0000-0002-2079-3410"},"institutions":[{"id":"https://openalex.org/I4210151430","display_name":"Institute of Aging","ror":"https://ror.org/044mbfr67","country_code":"CA","type":"facility","lineage":["https://openalex.org/I176337269","https://openalex.org/I4210151430"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Sergeant-Perthuis, Gr\u00e9goire","raw_affiliation_strings":["LCQB-AG"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LCQB-AG","institution_ids":["https://openalex.org/I4210151430"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002495502","display_name":"David Colliaux","orcid":"https://orcid.org/0000-0003-1898-4864"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Colliaux, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T12236","display_name":"Photoreceptor and optogenetics research","score":0.3467999994754791,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12236","display_name":"Photoreceptor and optogenetics research","score":0.3467999994754791,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11486","display_name":"Micro and Nano Robotics","score":0.14820000529289246,"subfield":{"id":"https://openalex.org/subfields/3104","display_name":"Condensed Matter Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10423","display_name":"Neurobiology and Insect Physiology Research","score":0.059700001031160355,"subfield":{"id":"https://openalex.org/subfields/2804","display_name":"Cellular and Molecular Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/phototaxis","display_name":"Phototaxis","score":0.6557000279426575},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5097000002861023},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5056999921798706},{"id":"https://openalex.org/keywords/synchronizing","display_name":"Synchronizing","score":0.444599986076355},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4300999939441681},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.40139999985694885},{"id":"https://openalex.org/keywords/ode","display_name":"Ode","score":0.3878999948501587},{"id":"https://openalex.org/keywords/sensory-system","display_name":"Sensory system","score":0.38350000977516174},{"id":"https://openalex.org/keywords/partially-observable-markov-decision-process","display_name":"Partially observable Markov decision process","score":0.3817000091075897},{"id":"https://openalex.org/keywords/observable","display_name":"Observable","score":0.3407000005245209}],"concepts":[{"id":"https://openalex.org/C200206546","wikidata":"https://www.wikidata.org/wiki/Q1510084","display_name":"Phototaxis","level":2,"score":0.6557000279426575},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5097000002861023},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5056999921798706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5037000179290771},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.49079999327659607},{"id":"https://openalex.org/C162932704","wikidata":"https://www.wikidata.org/wiki/Q1058791","display_name":"Synchronizing","level":3,"score":0.444599986076355},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4300999939441681},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41029998660087585},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.40139999985694885},{"id":"https://openalex.org/C34862557","wikidata":"https://www.wikidata.org/wiki/Q178985","display_name":"Ode","level":2,"score":0.3878999948501587},{"id":"https://openalex.org/C94487597","wikidata":"https://www.wikidata.org/wiki/Q11101","display_name":"Sensory system","level":2,"score":0.38350000977516174},{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.3817000091075897},{"id":"https://openalex.org/C32848918","wikidata":"https://www.wikidata.org/wiki/Q845789","display_name":"Observable","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C2777727519","wikidata":"https://www.wikidata.org/wiki/Q291827","display_name":"Chlamydomonas reinhardtii","level":4,"score":0.3370000123977661},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.33390000462532043},{"id":"https://openalex.org/C51955184","wikidata":"https://www.wikidata.org/wiki/Q1545585","display_name":"Stochastic differential equation","level":2,"score":0.33239999413490295},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.32739999890327454},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.3192000091075897},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3192000091075897},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.299699991941452},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2849999964237213},{"id":"https://openalex.org/C124223222","wikidata":"https://www.wikidata.org/wiki/Q2281940","display_name":"Chemical process","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.28380000591278076},{"id":"https://openalex.org/C38858127","wikidata":"https://www.wikidata.org/wiki/Q5441228","display_name":"Feed forward","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C9870796","wikidata":"https://www.wikidata.org/wiki/Q490481","display_name":"Turing","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C129537906","wikidata":"https://www.wikidata.org/wiki/Q7603913","display_name":"State variable","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C2776580309","wikidata":"https://www.wikidata.org/wiki/Q133008","display_name":"Chlamydomonas","level":4,"score":0.26930001378059387},{"id":"https://openalex.org/C111370547","wikidata":"https://www.wikidata.org/wiki/Q7451120","display_name":"Sensory cue","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C2776966350","wikidata":"https://www.wikidata.org/wiki/Q6935096","display_name":"Multistability","level":3,"score":0.26669999957084656},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2644999921321869},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C165287380","wikidata":"https://www.wikidata.org/wiki/Q2916569","display_name":"Foraging","level":2,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.26168","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26168","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.26168","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26168","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Living":[0],"systems":[1],"navigate":[2],"environments":[3],"using":[4],"noisy":[5],"and":[6,104,123,153,199],"incomplete":[7],"sensory":[8,38,197],"signals.":[9],"In":[10],"unicellular":[11],"algae,":[12],"phototaxis":[13],"is":[14],"often":[15],"modeled":[16],"as":[17,49,186],"a":[18,41,50,60,63,82,90,117,124],"mechanistic":[19],"run--tumble":[20,183],"process":[21,120],"driven":[22],"by":[23],"stimulus--response":[24],"rules.":[25],"However,":[26],"such":[27],"descriptions":[28],"overlook":[29],"how":[30,204],"organisms":[31],"actively":[32],"sample":[33,195],"their":[34],"environment":[35],"to":[36,173,194],"reduce":[37],"ambiguity.":[39],"From":[40],"minimal":[42,83],"cognition":[43],"perspective,":[44],"we":[45,58,143],"reframe":[46],"this":[47,56,178,181],"navigation":[48],"subjective,":[51],"information-driven":[52],"sensorimotor":[53],"process.":[54],"To":[55],"end,":[57],"propose":[59],"framework":[61],"linking":[62],"Partially":[64],"Observable":[65],"Markov":[66],"Decision":[67],"Process":[68],"(POMDP)":[69],"with":[70,101,149,158],"biochemical":[71,206],"reaction":[72],"dynamics.":[73],"Environmental":[74],"variables":[75],"are":[76],"hidden,":[77],"while":[78],"the":[79,145,155,168,192],"cell":[80,193],"updates":[81],"internal":[84,95],"state":[85],"from":[86],"each":[87],"observation":[88,119],"through":[89,108],"memoryless":[91],"Bayesian":[92],"step.":[93],"These":[94],"dynamics":[96,157],"balance":[97],"orienting":[98],"toward":[99],"light":[100],"exploratory":[102],"reorientation":[103],"can":[105,208],"be":[106],"implemented":[107],"Chemical-Reaction-Network":[109],"Ordinary":[110],"Differential":[111],"Equations":[112],"(CRN--ODEs).":[113],"Our":[114,165],"model":[115,166],"includes":[116],"biophysical":[118],"for":[121],"photoreception":[122],"chemically":[125],"computable":[126],"polynomial":[127],"bound":[128],"on":[129,137,177],"information":[130],"gain.":[131],"Using":[132],"Inverse":[133],"Reinforcement":[134],"Learning":[135],"(IRL)":[136],"30":[138],"experimentally":[139],"recorded":[140],"Chlamydomonas":[141],"trajectories,":[142],"infer":[144],"behavioral":[146],"objective":[147,174],"consistent":[148],"observed":[150],"phototactic":[151],"motion":[152],"benchmark":[154],"resulting":[156],"standard":[159],"Stochastic":[160],"Simulation":[161],"Algorithm":[162],"(SSA)":[163],"baselines.":[164],"reproduces":[167],"empirical":[169],"alignment-to-light":[170],"distribution,":[171],"comparable":[172],"SSA":[175],"baselines":[176],"dataset.":[179],"Within":[180],"framework,":[182],"alternation":[184],"emerges":[185],"an":[187],"information-acquisition":[188],"strategy:":[189],"tumbling":[190],"reorients":[191],"new":[196],"configurations":[198],"resolve":[200],"sensor":[201],"ambiguity,":[202],"demonstrating":[203],"intracellular":[205],"networks":[207],"support":[209],"adaptive":[210],"information-seeking":[211],"behavior":[212],"in":[213],"cellular":[214],"navigation.":[215]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-27T00:00:00"}
