Research on multi-agent collaborative hunting algorithm based on game theory and Q-learning for a single escaper

Abstract

The multi-agent collaborative hunting problem is a typical problem in multi-agent coordination and collaboration research. Aiming at the multi-agent hunting problem with learning ability, a collaborative hunt method based on game theory and Q-learning is proposed. Firstly, a cooperative hunting team is established and a game model of cooperative hunting is built. Secondly, through the learning of the escaper’s strategy choice, the trajectory of the escaper’s limited T-step cumulative reward is established, and the trajectory is adjusted to the hunter’s strategy set. Finally, the Nash equilibrium solution is obtained by solving the cooperative hunt game, and each hunter executes the equilibrium strategy to complete the hunt task. C# simulation experiment shows that under the same conditions, this method can effectively solve the hunting problem of a single runaway with learning ability in the obstacle environment, and the comparative analysis of experimental data shows that the efficiency of this method is better than other methods.