Multi-Agent Reinforcement Learning Autonomous Task Planning for Deep Space Probes

SUN Zeyi1, WANG Bin1,2, HU Xinyue1, XIONG Xin1,2, JIN Huaiping1,2

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Journal of Deep Space Exploration ›› 2024, Vol. 11 ›› Issue (3) : 244-255. DOI: 10.15982/j.issn.2096-9287.2024.20230159

Multi-Agent Reinforcement Learning Autonomous Task Planning for Deep Space Probes

  • SUN Zeyi1, WANG Bin1,2, HU Xinyue1, XIONG Xin1,2, JIN Huaiping1,2
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Abstract

To meet the requirements for autonomy,rapidity,and adaptability in the collaborative planning of each subsystem during the attachment mission of a deep space probe,a collaborative planning strategy based on proximal policy optimization method and multi-agent reinforcement learning was proposed. By combining the single-agent proximal policy optimization algorithm with the hybrid collaborative mechanism of multi-agent,a multi-agent autonomous task planning model was designed. The noise-regularized advantage value ws introduced to solve the problem of overfitting in the collaborative strategy of multi-agent centralized training. Simulation results show that the multi-agent reinforcement learning collaborative autonomous task planning method can intelligently optimize the collaboration strategy of small celestial body attachment missions according to real-time environmental changes,and compared with the previous algorithm,it improves the success rate of task planning and quality of planning solutions,and shortens the time of task planning.

Keywords

multi-agent reinforcement learning / autonomous task planning of deep space exploration / proximal policy optimization / small celestial body attachment

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SUN Zeyi, WANG Bin, HU Xinyue, XIONG Xin, JIN Huaiping. Multi-Agent Reinforcement Learning Autonomous Task Planning for Deep Space Probes. Journal of Deep Space Exploration, 2024, 11(3): 244‒255 https://doi.org/10.15982/j.issn.2096-9287.2024.20230159

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