Patent ID: 11948079
Assignee: nan
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A multi-agent coordination method, comprising:
performing a plurality of data collections on N agents to collect E sets of data, N and E being integers greater than 1; and
optimizing neural networks of the N agents using reinforcement learning based on the E sets of data,
wherein each data collection comprises:
randomly selecting a first coordination pattern from a plurality of predetermined coordination patterns;
obtaining N observations after the N agents act on an environment in the first coordination pattern;
determining a first probability and a second probability that a current coordination pattern is the first coordination pattern based on the N observations; and
determining a pseudo reward based on the first probability and the second probability, and

the E sets of data comprise: a first coordination pattern label indicating the first coordination pattern, the N observations, and the pseudo reward,
wherein the N agents are N robot agents, the environment is a real-world environment, and the N robot agents apply, based on action vectors determined by the optimized neural networks, actions corresponding to the action vectors to the real-world environment, and
said determining the pseudo reward based on the first probability and the second probability comprises:
calculating the pseudo reward based on:, r
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where z represents the first coordination pattern, rz represents the pseudo reward, x represents a set of the N observations, x(i) represents an i-th observation among the N observations, f( ) represents a feature extraction function, qøi(z|f(x(i))) represents the first probability that the current coordination pattern is the first coordination pattern z based on the i-th observation x(i), and qø(z|f(x)) represents the second probability that the current coordination pattern is the first coordination pattern z based on the set of the N observations x.