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Multi-agent Deep Reinforcement Learning for Microgrid Energy Scheduling

In this paper, we study the energy scheduling problem of solar-powered aircraft microgrid with multi-agent deep reinforcement learning algorithm under a centralized training and decentralized execution mechanism. This problem is modeled as a multi-agent Markov decision process and embedded in a multi-agent proximal policy optimization framework. A scheduling policy is proposed by offline training and then utilized for online implementation. Furthermore, two forms of action spaces are constructed to explore a more favorable result. Numerical experiments show that the algorithm enables us to obtain an effective policy in solving the energy scheduling problem.