Multi-Agent deep reinforcement learning framework and method based on state classification and assignment

The invention belongs to the field of artificial intelligence, and particularly relates to a multi-agent deep reinforcement learning framework and method based on state classification and assignment. Comprising the steps that M1 is executed, generated environment virtual state samples are classified, an assignment problem is solved, and a state classification model F and an assignment rule A of an agent are obtained; the method comprises the following steps: setting network structures of all agents in Multi-Agents, and learning modes, action selection strategies, updating modes and reward calculation methods of all agents in Multi-Agents; and executing M2, firstly initializing a plurality of homogeneous agents conforming to the setting of the above steps, then initializing the environment, and finally interacting the plurality of agents with the environment, learning action strategies and updating respective networks. According to the method, a complex environment state is decomposed into a plurality of simpl