A Data-Driven Modeling Method for Game Adversity Agent

doi:10.16182/j.issn1004731x.joss.20-FZ0532

Journal of System Simulation ›› 2021, Vol. 33 ›› Issue (12): 2838-2845.doi: 10.16182/j.issn1004731x.joss.20-FZ0532

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A Data-Driven Modeling Method for Game Adversity Agent

Zeng Bi^1,2, Fang Xiao¹, Kong Deshuai³, Song Xiangxiang¹, Jia Zhengxuan^1,2, Lin Tingyu^1,2

1. Beijing Simulation Center, Beijing 100854, China;
2. Beijing Institute of Electronic System, Beijing 100854, China;
3. China Aerospace Science and Industry Corporation Limited, Beijing 100048, China

Received:2020-04-01 Revised:2021-06-08 Online:2021-12-18 Published:2022-01-13

Abstract

Abstract: Aiming at the problems of collaborative modeling of formation behavior and intelligent generation of decision-making in complex confrontation scenarios, based on the serious game to simulate the confrontation scenarios of complex maritime equipment against the air, this paper proposes a data-driven modeling method for game agent and uses a distributed modeling technology of parallel adversarial scenarios and opportunistic decision making technology of smart targets to achieve agent modeling. It provides support for the further exploration of multi-objective collaborative modeling in complex confrontation scenarios. The simulation results show that deep reinforcement learning algorithms can provide a basis for the modeling of agents dexterous strategies.

Key words: deep reinforcement learning, data-driven, distributed training, opportunistic decision making

CLC Number:

TP391.9

Zeng Bi, Fang Xiao, Kong Deshuai, Song Xiangxiang, Jia Zhengxuan, Lin Tingyu. A Data-Driven Modeling Method for Game Adversity Agent[J]. Journal of System Simulation, 2021, 33(12): 2838-2845.

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A Data-Driven Modeling Method for Game Adversity Agent

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