Multi-Satellite Orbital Pursuit-Evasion Game Method Based on Behavior Trees
Multi-agent reinforcement learning is an effective approach for solving spatial pursuit-evasion games.In multi-star pursuit-evasion scenarios,however,there are challenges such as long training time and difficulty in convergence.This paper propo-ses a multi-star orbital pursuit-evasion method based on behavior trees,decomposing the complex pursuit-evasion problem invol-ving multiple targets into individual pursuit-evasion problems for each target.By utilizing behavior trees to construct the framework for task allocation and game decision-making in multi-star pursuit-evasion scenarios,the optimal task allocation model is estab-lished with the objective of maximizing the probability of successful pursuit.Genetic algorithms are employed for solving,enabling rapid decomposition of multi-star pursuit-evasion tasks.For the allocated pursuit tasks,each satellite autonomously selects game strategies obtained through training with the Multi-Agent Deep Deterministic Policy Gradient algorithm.The results demonstrate that the proposed method effectively decomposes multi-star orbital game tasks and successfully achieves target pursuit under the guidance of behavior trees.
multi-satellite orbital pursuit-evasion gamebehavior treetask allocationmulti-agent reinforcement learning
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