An intelligent generating method for multi-target attacking strategy based on environment-aware deep reinforcement learning

Jingang Wang; Binbin Yan; Peng Wang; Duo Chen; Huachun Wang; Xinzhu Sang

doi:10.1117/12.2624431

27 March 2022 An intelligent generating method for multi-target attacking strategy based on environment-aware deep reinforcement learning

Jingang Wang, Binbin Yan, Peng Wang, Duo Chen, Huachun Wang, Xinzhu Sang

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Proceedings Volume 12169, Eighth Symposium on Novel Photoelectronic Detection Technology and Applications; 121695O (2022) https://doi.org/10.1117/12.2624431
Event: Eighth Symposium on Novel Photoelectronic Detection Technology and Applications, 2021, Kunming, China

Abstract

With the development of Deep Reinforcement Learning (DRL), the applications of intelligent decisions in nonsymmetric information games have has become realizable. However, it is still a challenging task in DRL for its difficulties of building efficient exploration and action-reward mechanism, especially in an environment with multiple targets. To address this problem, a generating method of multi-target attacking strategy based on environment-aware DRL is proposed in this paper. Our proposed method consists of two stages in the agent learning process. The first stage is an environmentaware module for predicting the motion trajectories of multiple targets by using the optical flow estimation. The second stage is a decision-making module for predicting appropriate actions such as choosing angles and attacking by using the improved Deep Q-Network (DQN). To solving the problem of sparse rewards in the learning process, the motion trajectories predicted in the first stage are used to build reward trajectories for accelerating the convergence rate of the algorithm in the second stage. The experiments indicate that the proposed method can effectively generate multi-target attacking strategy in our self-built simulation environment. Our method can also provide a novel perspective of intelligent decisions in three-dimension space.

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Jingang Wang, Binbin Yan, Peng Wang, Duo Chen, Huachun Wang, and Xinzhu Sang "An intelligent generating method for multi-target attacking strategy based on environment-aware deep reinforcement learning", Proc. SPIE 12169, Eighth Symposium on Novel Photoelectronic Detection Technology and Applications, 121695O (27 March 2022); https://doi.org/10.1117/12.2624431

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KEYWORDS

Strategic intelligence

Detection and tracking algorithms

Environmental sensing

Motion models

Neural networks

Cognitive modeling

Performance modeling

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