Abstract:The integration of wargaming and an intelligent algorithm has become a research hotspot in the field of military application. Using deep reinforcement learning (DRL) to realize the intellectualized decision-making process in simulation deduction can significantly reduce the impact of human experience on the decision-making process and improve deduction efficiency and flexibility. Limited by its long training time and high computational cost, the existing decision-making model based on the DRL algorithm cannot meet the requirement of combat tasks for real-time performance. This study introduces an intelligent deduction method based on the lightweight binary neural network-deep deterministic policy gradient (BN-DDPG) algorithm. According to deduction rules, the Markov decision process is used to describe the decision behavior during deduction. Relying on the actor-critic system, an agent training network is constructed, in which the actor network uses a custom hybrid binary neural network to reduce the amount of calculation. At the same time, a double-buffer-pool structure is built according to the status and return value of empirical samples, and sampling is performed by the method of priority extraction of environmental similarity for higher training efficiency. Finally, an example is verified on a self-developed simulation deduction platform. The results show that the BN-DDPG algorithm can simplify the model training process, accelerate the convergence of the model, and significantly improve the accuracy of deduction and decision-making.