Abstract: The present disclosure discloses a multi-agent coordination method. The method includes: performing multiple data collections on N agents to collect E sets of data, where N and E are integers greater than 1; and optimizing neural networks of the N agents using reinforcement learning based on the E sets of data. Each data collection includes: randomly selecting a first coordination pattern from multiple 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. The E sets of data include: a first coordination pattern label indicating the first coordination pattern, the N observations, and the pseudo reward.

Abstract: The present disclosure discloses an unsupervised reinforcement learning method and apparatus based on Wasserstein distance. The method includes: obtaining a state distribution in a trajectory obtained with guidance of a current policy of an agent; calculating a Wasserstein distance between the state distribution and a state distribution in a trajectory obtained with another historical policy, and calculating a pseudo reward of the agent based on the Wasserstein distance, replacing a reward fed back from an environment in a target reinforcement learning framework with the pseudo reward, and guiding the current policy of the agent to keep a large distance from the other historical policy. The method uses Wasserstein distance to encourage an algorithm in an unsupervised reinforcement learning framework to obtain diverse policies and skills through training.

Abstract: The present disclosure discloses a multi-agent coordination method. The method includes: performing multiple data collections on N agents to collect E sets of data, where N and E are integers greater than 1; and optimizing neural networks of the N agents using reinforcement learning based on the E sets of data. Each data collection includes: randomly selecting a first coordination pattern from multiple 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. The E sets of data include: a first coordination pattern label indicating the first coordination pattern, the N observations, and the pseudo reward.