Revisiting a Three-Player Pursuit-Evasion Game

Szőts, János; Savkin, Andrey V.; Harmati, István

doi:10.1007/s10957-021-01899-8

Cited by 6 publications

(1 citation statement)

References 14 publications

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“… Nath and Ghose (2022) and Zhang and Zha (2018) conducted pursuit-evasion game research under certain objectives or constraints in the two-dimensional plane. The differential game strategy in optimal control ( Fuchs et al, 2018 ; Szots et al, 2021 ; Yan et al, 2021 ; Zhang et al, 2023 ) was utilized to solve the three-player pursuit-evasion game. Wang et al (2020) , Wan et al (2021) , and Hamidoglu (2023) each applied different intelligent algorithms to achieve evasion, respectively.…”

Section: Introductionmentioning

confidence: 99%

Intelligent maneuver strategy for hypersonic vehicles in three-player pursuit-evasion games via deep reinforcement learning

Yan,

Jiang,

et al. 2024

Front. Neurosci.

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Aiming at the rapid development of anti-hypersonic collaborative interception technology, this paper designs an intelligent maneuver strategy of hypersonic vehicles (HV) based on deep reinforcement learning (DRL) to evade the collaborative interception by two interceptors. Under the meticulously designed collaborative interception strategy, the uncertainty and difficulty of evasion are significantly increased and the opportunity for maneuvers is further compressed. This paper, accordingly, selects the twin delayed deep deterministic gradient (TD3) strategy acting on the continuous action space and makes targeted improvements combining deep neural networks to grasp the maneuver strategy and achieve successful evasion. Focusing on the time-coordinated interception strategy of two interceptors, the three-player pursuit and evasion (PE) problem is modeled as the Markov decision process, and the double training strategy is proposed to juggle both interceptors. In reward functions of the training process, the energy saving factor is set to achieve the trade-off between miss distance and energy consumption. In addition, the regression neural network is introduced into the deep neural network of TD3 to enhance intelligent maneuver strategies’ generalization. Finally, numerical simulations are conducted to verify that the improved TD3 algorithm can effectively evade the collaborative interception of two interceptors under tough situations, and the improvements of the algorithm in terms of convergence speed, generalization, and energy-saving effect are verified.

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