Research on multi-agent game confrontation combined with prior knowledge
The complex adversarial environment without real-time reward is the current research hot spot in the field of deep reinforcement learning(DRL).In such environment,the use of deep reinforcement learning algorithm alone in general leads to a lower convergence speed and unsatisfactory performance.In this regard,this paper proposes an intelligent game process framework based on the combination of prior knowledge and deep reinforcement learn-ing,and designs three modules of data processing,enhancement mechanism and action decision-making to improve both the convergence speed and the countermeasure effect under complex confrontation environment through three enhancement mechanisms including threat assessment,task scheduling and loss ratio.The simulation results on the DataCastle(DC)platform show that the agent trained by the proposed intelligent game process framework has a fast convergence speed and higher winning rate than the agent only based on deep reinforcement learning.
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