Task Scheduling for Single Satellite Observation Based on Meta-Reinforcement Learning

What is it about?

A mathematical model for satellite scheduling based on the Markov Decision Process (MDP) was developed, tailored to the characteristics of EOSSP. The problem was solved using meta-reinforcement learning (meta-RL) with the Proximal Policy Optimization (PPO) algorithm. Comparisons with other algorithms indicate that the meta-RL algorithm demonstrates advantages such as fast convergence, strong generalization ability, short execution time, and high overall rewards in large-scale scheduling problems.

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