Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility
Flora Charbonnier,
Thomas Morstyn,
Malcolm D. McCulloch
Abstract:This paper proposes a novel scalable type of multi-agent reinforcement learning-based coordination for distributed residential energy. Cooperating agents learn to control the flexibility offered by electric vehicles, space heating and flexible loads in a partially observable stochastic environment. In the standard independent Q-learning approach, the coordination performance of agents under partial observability drops at scale in stochastic environments.Here, the novel combination of learning from off-line con… Show more
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