基于深度強(qiáng)化學(xué)習(xí)的多智能體路徑規(guī)劃

基于深度強(qiáng)化學(xué)習(xí)的多智能體路徑規(guī)劃基于深度強(qiáng)化學(xué)習(xí)的多智能體路徑規(guī)劃

摘要：多智能體系統(tǒng)中的路徑規(guī)劃問(wèn)題是現(xiàn)代機(jī)器人技術(shù)研究的重要課題。針對(duì)傳統(tǒng)路徑規(guī)劃技術(shù)因受環(huán)境地形限制、難以適應(yīng)復(fù)雜環(huán)境等問(wèn)題，在本文中，我們提出了一種基于深度強(qiáng)化學(xué)習(xí)的多智能體路徑規(guī)劃算法。首先，我們將問(wèn)題建模為馬爾可夫決策過(guò)程，并通過(guò)深度神經(jīng)網(wǎng)絡(luò)訓(xùn)練智能體在不同狀態(tài)下的最佳策略。其次，我們提出了一種基于遺傳算法的策略集結(jié)構(gòu)，通過(guò)對(duì)不同策略的組合和優(yōu)化，獲得了更為魯棒的路徑規(guī)劃結(jié)果。最后，我們?cè)诙喾N仿真環(huán)境下進(jìn)行實(shí)驗(yàn)驗(yàn)證，以證明所提出算法的有效性和魯棒性。實(shí)驗(yàn)結(jié)果表明，本文所提出的基于深度強(qiáng)化學(xué)習(xí)的多智能體路徑規(guī)劃算法可以在多智能體協(xié)同環(huán)境中快速、準(zhǔn)確地完成路徑規(guī)劃任務(wù)。

關(guān)鍵詞：路徑規(guī)劃；多智能體系統(tǒng)；深度強(qiáng)化學(xué)習(xí)；馬爾可夫決策過(guò)程；遺傳算法

Abstract:Thepathplanningprobleminmulti-agentsystemsisanimportantissueinmodernroboticsresearch.Inthispaper,weproposeamulti-agentpathplanningalgorithmbasedondeepreinforcementlearningtoaddressthelimitationsoftraditionalpathplanningtechniques,suchasenvironmentalterrainconstraintsanddifficultyinadaptingtocomplexenvironments.First,wemodeltheproblemasaMarkovdecisionprocessandtraintheagents'optimalpoliciesunderdifferentstatesbyusingdeepneuralnetworks.Secondly,weproposeastrategysetstructurebasedongeneticalgorithmstoobtainmorerobustpathplanningresultsthroughcombinationandoptimizationofdifferentstrategies.Finally,weconductexperimentalvalidationinmultiplesimulationenvironmentstodemonstratetheeffectivenessandrobustnessoftheproposedalgorithm.Theexperimentalresultsshowthatthemulti-agentpathplanningalgorithmbasedondeepreinforcementlearningproposedinthispapercanquicklyandaccuratelycompletepathplanningtasksinmulti-agentcollaborativeenvironments.

Keywords:pathplanning;multi-agentsystem;deepreinforcementlearning;Markovdecisionprocess;geneticalgorithInrecentyears,significantattentionhasbeengiventotheproblemofpathplanninginmulti-agentsystemsduetoitsessentialroleinvariousapplications,includingrobotics,automatedtransportationsystems,andswarmintelligence.Tosolvethisproblem,manyapproacheshavebeenproposed,suchasgeneticalgorithms,neuralnetworks,andreinforcementlearning.

Inthispaper,weproposedamulti-agentpathplanningalgorithmbasedondeepreinforcementlearning.Thealgorithmaimstofindanoptimalpathformultipleagentstoachieveacommongoalwhileavoidingcollisionswitheachotherandobstaclesintheenvironment.Toachievethis,ouralgorithmusesaMarkovdecisionprocess(MDP)tomodelthepathplanningproblemasasequentialdecision-makingprocess.Wethentrainadeepreinforcementlearningagentforeachagentinthesystem,whichlearnstomakeoptimaldecisionsbasedontheobservationsofitslocalenvironment.

Tovalidatetheperformanceofourproposedalgorithm,weconductedexperimentsinmultiplesimulationenvironmentswithdifferentnumbersofagentsandobstacleconfigurations.Theresultsshowthatouralgorithmcanquicklyandaccuratelycompletepathplanningtasksinmulti-agentcollaborativeenvironments.Morespecifically,comparedwithotherexistingapproaches,ouralgorithmshowshighersuccessrates,lowercollisionrates,andshorterplanningtimes.

Furthermore,wealsoconductedsensitivityanalysistoexaminetherobustnessandadaptabilityofouralgorithmunderdifferentscenarios.Theresultsshowthatouralgorithmcanmaintaingoodperformanceinvariousscenarioswithdifferentagentnumbers,obstacleshapes,andsizes.

Insummary,ourproposedmulti-agentpathplanningalgorithmbasedondeepreinforcementlearningprovidesapromisingsolutiontothecomplexandchallengingproblemofmulti-agentpathplanning.Theresultsdemonstratetheeffectivenessandrobustnessofouralgorithminvariouspracticalscenarios,highlightingitspotentialinreal-worldapplicationsFurthermore,ouralgorithmcanhandlescenarioswithvaryingagentnumbers.Wehavetestedouralgorithmwithupto20agents,andithasshowngoodperformance.Inscenarioswithalargenumberofagents,ouralgorithmeffectivelybalancestheexplorationversusexploitationtrade-off,leadingtoefficientandcollision-freepaths.

Ouralgorithmisalsorobusttodifferentobstacleshapesandsizes.Wehavetestedouralgorithmwithvariousobstacleshapes,suchascircles,rectangles,andirregularshapes.Ouralgorithmconsistentlygeneratedcollision-freepathsbyavoidingtheobstacles.Evenwhentheobstacleswereplacedincomplexarrangements,ouralgorithmwasstillabletofindsafeandefficientpathsfortheagents.

Moreover,ouralgorithmisreadilyapplicabletoscenarioswithdifferentroadnetworktopologies.Forexample,inscenarioswithmultiplenarrowpaths,ouralgorithmtendstoguidetheagentstowardslesscrowdedareastoavoidcongestion.Inscenarioswithopenareas,ouralgorithmenablesfastandefficientmovementsbyallowingtheagentstotakestraightpaths.

Inaddition,ouralgorithmcanhandlesituationswhereagentshavedifferentproperties,suchasdifferentspeedsorpriorities.Wehavetestedouralgorithminscenarioswithagentsofvaryingspeeds,andithasshowntheabilitytoeffectivelycoordinatethemovementsoftheagents.Similarly,inscenarioswithagentsofdifferentpriorities,ouralgorithmwasabletoassignprioritiesandgeneratecollision-freepathsaccordingly.

Finally,ouralgorithmcanhandlescenarioswithdynamicobstacles.Wehavetestedouralgorithminscenarioswhereobstaclesmoveatdifferentspeedsandindifferentdirections.Ouralgorithmquicklyadaptstothechangesintheenvironmentandgeneratescollision-freepathsfortheagents.

Inconclusion,ourproposedmulti-agentpathplanningalgorithmbasedondeepreinforcementlearningisshowntobeeffectiveandrobustinvariouspracticalscenarios,highlightingitspotentialforreal-worldapplications.Withfurtherdevelopmentandoptimization,ouralgorithmcanbeappliedtovariousreal-worldscenariosandprovidevaluablesolutionsformulti-agentpathplanningproblemsOnepotentialapplicationofourmulti-agentpathplanningalgorithmisinautonomousdriving.Withtheincreasingpopularityofself-drivingcars,ensuringsafeandefficientnavigationofmultipleautonomousvehiclesontheroadisbecomingacriticalchallenge.Ouralgorithmcanbeusedtogenerateoptimalpathsformultiplevehicles,takingintoaccounttheirindividualgoals,whileavoidingcollisions.

Anotherpossibleapplicationisinroboticswarmsforsearchandrescuemissions.Insuchscenarios,multiplerobotsneedtoco-operateandcollaboratetonavigatethroughunfamiliar,hazardousenvironmentstolocateandrescuesurvivors.Ouralgorithmcangeneratecollision-freepathsfortherobots,whilealsoadaptingtochangesintheenvironment,makingitidealforsuchapplications.

Overall,ourproposedalgorithmpresentsapracticalandefficientsolutionforthecomplexproblemofmulti-agentpathplanning,withpotentialapplicationsacrossvariousfields.Byleveragingthepowerofdeepreinforcementlearning