A 2v2 Three-Country Killing Multi-Agent Game Method Based on Reinforcement Learning
Deep reinforcement learning has achieved great success in dealing with sequential decision-making and strategy exploration,and most of them are inspired by in-game research,and its application field has expanded from single-agent scenarios to multi-agent scenarios.Solitaire-based multiplayer strategy games are a multi-agent system,but there are few existing studies,and most of them come from Doudi Landlord and Texas Hold'em.In order to expand the research of multi-agent strategy games based on cards,this paper proposes a 2v2 three-country killing multi-agent game method(SGS-MAPG)based on reinforcement learning,which builds a 2v2 battle game scene with the background of three-kingdom killing game as the experimental environment,models cooperative multiple agents based on the idea of strategy gradient,and includes teamwork and confrontation of multi-agent systems in its decision-making process,which solves the problem of instability in multiple agent environments.Through computer simulation of the battle process,this method enables the agent to be trained to have good learning and decision-making ability,and can try to obtain more final team rewards than the basic algorithm,and get at least 12%higher win rate.
Deep reinforcement learningMulti-agentThree kingdoms killing game environmentCooperative competition
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