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

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.