Application of advanced tree search and proximal policy optimization on formula-E race strategy development

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dc.contributor.authorLiu, Xuze
dc.contributor.authorFotouhi, Abbas
dc.contributor.authorAuger, Daniel J.
dc.date.accessioned2022-03-08T14:05:33Z
dc.date.available2022-03-08T14:05:33Z
dc.date.issued2022-02-25
dc.description.abstractEnergy and thermal management is a crucial element in Formula-E race strategy development. Most published literature focuses on the optimal management strategy for a single lap and results in sub-optimal solutions to the larger multi-lap problem. In this study, two Monte Carlo Tree Search (MCTS) enhancement techniques are proposed for multi-lap Formula-E racing strategy development. It is shown that using the bivariate Gaussian distribution enhancement, race finishing time improves by at least 0.25% and its variance reduces by more than 26%. Compared to the published conventional MCTS technique used in multi-lap problems, this proposed technique is proved to bring a remarkable enhancement with no additional computational time cost. By further enhancing the MCTS using proximal policy optimization, the final product is capable of generating more than 0.5% quicker race time solutions and improving the consistency by over 90% which makes it a very suitable method particularly when enough training time is guaranteeden_UK
dc.identifier.citationLiu X, Fotouhi A, Auger D. (2022) Application of advanced tree search and proximal policy optimization on formula-E race strategy development, Expert Systems with Applications, Volume 197, July 2022, Article number 116718en_UK
dc.identifier.issn0957-4174
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2022.116718
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17632
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectEnergy managementen_UK
dc.subjectFormula-E race strategyen_UK
dc.subjectMonte Carlo Tree searchen_UK
dc.subjectProximal policy optimizationen_UK
dc.titleApplication of advanced tree search and proximal policy optimization on formula-E race strategy developmenten_UK
dc.typeArticleen_UK

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