A survey: evolutionary deep learning

Li, Yifan; Liu, Jing

doi:10.1007/s00500-023-08316-4

Cited by 2 publications

(1 citation statement)

References 102 publications

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“…Moreover, the deep reinforcement learning (DRL) method combining DL with RL has been proposed. DRL [22] is considered as a strong artificial intelligent paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Therefore, artificial intelligence which was developed using DRL can enable future UAVs to learn autonomously, make self‐improvement and self‐control, and be able to think and participate in combat in a human way of thinking, so as to gain air dominance.…”

Section: Introductionmentioning

confidence: 99%

Manoeuvre decision‐making of unmanned aerial vehicles in air combat based on an expert actor‐based soft actor critic algorithm

Bai

Liang

et al. 2023

CAAI Trans on Intel Tech

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The demand for autonomous motion control of unmanned aerial vehicles in air combat is boosted as taking the initiative in combat appears more and more crucial. Unmanned aerial vehicles inability to manoeuvre autonomously during air combat that features highly dynamic and uncertain manoeuvres of the enemy; however, limits their combat capabilities, which proves to be very challenging. To meet the challenge, this article proposes an autonomous manoeuvre decision model using an expert actor‐based soft actor critic algorithm that reconstructs empirical replay buffer with expert experience. Specifically, the algorithm uses a small amount of expert experience to increase the diversity of the samples, which can largely improve the exploration and utilisation efficiency of deep reinforcement learning. And to simulate the complex battlefield environment, a one‐to‐one air combat model is established and the concept of missile's attack region is introduced. The model enables the one‐to‐one air combat to be simulated under different initial battlefield situations. Simulation results show that the expert actor‐based soft actor critic algorithm can find the most favourable policy for unmanned aerial vehicles to defeat the opponent faster, and converge more quickly, compared with the soft actor critic algorithm.

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Section: Introductionmentioning

confidence: 99%