Citation:
He 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
Abstract:
This 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.