Battery scheduling method, system and device based on deep reinforcement learning, and medium

The invention discloses a battery scheduling method, system and device based on deep reinforcement learning, and a medium. The method comprises the steps of determining a current reinforcement learning parameter based on current use feature data of a battery replacement cabinet and current battery feature data; acquiring a first battery distribution number of the target point location; estimating a second battery distribution number according to the historical battery characteristic data; and based on the first battery distribution number and the second battery distribution number, determining the battery distribution number of the target point location. According to the method, the multi-feature dimension information is fully utilized, and the multi-feature dimension information and the deep reinforcement learning model are utilized to output different battery type quantity distribution strategies in each battery replacement cabinet point location of each city, so that the variance of battery distribution is