Multi-agent Game Strategy Generation Method Based on Hierarchical Reinforcement Learning
In traditional multi-agent confrontation strategy generation method based on deep reinforcement learning,a"decentralized"framework is adopted,in which each agent generates strategies and makes decisions based on partial observable information,lacking the ability to generate confrontation strategy from the whole observable information and greatly limiting the decision-making ability.To address this disadvantage,an improved method for generating multi-agent game strategies based on hierarchical reinforcement learning is proposed.First,decision mapping from observation information to overall value is constructed based on hierarchical reinforcement learning,optimization problems are formulated with maximization of overall value as the objective,and the process of strategy optimization is derived,providing theoretical basis for the subsequent design of framework structure and method implementation.Then,based on the decision mapping and optimization problems,a model framework is designed using neural networks,and detailed explanations are provided for the top-level strategy control model and individual strategy execution model.Furthermore,detailed training processes and algorithm flows are presented based on strategy optimization method.Finally,the performance of the proposed method is compared with traditional multi-agent methods using StarCraft Multi-Agent Challenge(SMAC)environment.Experimental results demonstrate that the method effectively generates confrontation strategies,enabling heterogamous multi-agent systems to defeat preset opponent strategies,and the performance is significantly improved as compared to traditional multi-agent reinforcement learning method.
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