Multi-agent Game Strategies Generation Method Based on Static Games and Genetic Algorithms
In the process of generating multi-agent collaborative confrontation strategies,sparse rewards and numerous neural network parameters often lead to slow strategy generation.To rapidly generate confrontation strategies for specific scenarios,a method for multi-agent game strategies generation based on static games and genetic algorithms is proposed.Leveraging the concept of static games,the evolution of the Markov decision process maps the strategies to a sequence of actions,simplifying the principle of strategy mapping.Subsequently,mathematical modeling is applied to the strategy optimization problem.Using the confrontation result as the objective function and optimizing it based on the action set,the method can acquire strategies for optimal confrontation results through optimization.Then,a strategy optimization framework is presented,and genetic algorithms are improved to achieve rapid parallel optimization for multi-agent game strategies.Experimental results demonstrate that,compared to classical multi-agent reinforcement learning methods,the proposed method efficiently generates strategies for multi-agent games.
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