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This paper presents a reinforcement learning (RL) algorithm for multi-agent patrol tasks, which can be thought of as a dynamic programming problem with stochastic demands. We define the cover rate as the reward, the multi-agent physical positions including edges and nodes as the state, and the nodes adjacent to the agent as the action to model the patrol task. The modeling of this problem is totally...
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