Computational missile guidance: a deep reinforcement learning approach

He, ShaomingShin, HyosangTsourdos, Antonios2021-07-052021-07-052021-06-28He 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. 571-5822327-3097https://doi.org/10.2514/1.I010970https://dspace.lib.cranfield.ac.uk/handle/1826/16843This 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Deep Deterministic Policy GradientProportional navigation guidanceComputational missile guidance: a deep reinforcement learning approachArticle