Turn-Based Sequential Game Under Impulsive Control with Perceptual Delay

This paper investigates the pursuit-evasion game under impulsive control with perceptual delay. A turn-based sequence game model is proposed, enabling both the pursuer and the evader to alternately make decisions and better capture time-dependent effects on game states and strategies. To enhance evasive performance, a sunlight-aligned countermeasure strategy is introduced, leveraging the relative Sun-spacecraft position. Additionally, an improved Monte Carlo Tree Search (MCTS)-based pursuit-evasion control method is proposed, incorporating both process and terminal rewards to address sparse reward challenges. Simulation results show that the improved MCTS method significantly prolongs the evader's survival and increases the minimum inter-spacecraft distance, even under disadvantageous scenarios. The sunlight-aligned countermeasure strategy further improves spatial advantage against highly maneuverable threats.

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Publication Details

Published
2025-07-04
DOI
https://doi.org/10.1109/fasta65681.2025.11138511
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
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article

Turn-Based Sequential Game Under Impulsive Control with Perceptual Delay

Jianfa Wu, Wanying Gao, Chunling Wei
Reinforcement Learning in Robotics
article

Turn-Based Sequential Game Under Impulsive Control with Perceptual Delay

Jianfa Wu, Wanying Gao, Chunling Wei
article en

Abstract

This paper investigates the pursuit-evasion game under impulsive control with perceptual delay. A turn-based sequence game model is proposed, enabling both the pursuer and the evader to alternately make decisions and better capture time-dependent effects on game states and strategies. To enhance evasive performance, a sunlight-aligned countermeasure strategy is introduced, leveraging the relative Sun-spacecraft position. Additionally, an improved Monte Carlo Tree Search (MCTS)-based pursuit-evasion control method is proposed, incorporating both process and terminal rewards to address sparse reward challenges. Simulation results show that the improved MCTS method significantly prolongs the evader's survival and increases the minimum inter-spacecraft distance, even under disadvantageous scenarios. The sunlight-aligned countermeasure strategy further improves spatial advantage against highly maneuverable threats.

Openalex Percentile: Top 87%
Reinforcement Learning in Robotics
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