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

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