Computational missile guidance: a deep reinforcement learning approach

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dc.contributor.advisor
dc.contributor.authorHe, Shaoming
dc.contributor.authorShin, Hyosang
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2021-07-05T15:20:23Z
dc.date.available2021-07-05T15:20:23Z
dc.date.issued2021-06-28
dc.description.abstractThis paper aims to examine the potential of using the emerging deep reinforcement learning techniques in missile guidance applications. To this end, a Markovian decision process that enables the application of reinforcement learning theory to solve the guidance problem is formulated. A heuristic way is used to shape a proper reward function that has tradeoff between guidance accuracy, energy consumption, and interception time. The state-of-the-art deep deterministic policy gradient algorithm is used to learn an action policy that maps the observed engagements states to a guidance command. Extensive empirical numerical simulations are performed to validate the proposed computational guidance algorithm.en_UK
dc.identifier.citationHe S, Shin H-S, Tsourdos A. (2021) Computational missile guidance: a deep reinforcement learning approach. Journal of Aerospace Information Systems, Volume 18, Number 8, August 2021, pp. 571-582
dc.identifier.issn2327-3097
dc.identifier.urihttps://doi.org/10.2514/1.I010970
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16843
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectDeep Deterministic Policy Gradienten_UK
dc.subjectProportional navigation guidanceen_UK
dc.titleComputational missile guidance: a deep reinforcement learning approachen_UK
dc.typeArticleen_UK

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