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- W4308337575 abstract "According to the requirements of the live-virtual-constructive (LVC) tactical confrontation (TC) on the virtual entity (VE) decision model of graded combat capability, diversified actions, real-time decision-making, and generalization for the enemy, the confrontation process is modeled as a zero-sum stochastic game (ZSG). By introducing the theory of dynamic relative power potential field, the problem of reward sparsity in the model can be solved. By reward shaping, the problem of credit assignment between agents can be solved. Based on the idea of meta-learning, an extensible multi-agent deep reinforcement learning (EMADRL) framework and solving method is proposed to improve the effectiveness and efficiency of model solving. Experiments show that the model meets the requirements well and the algorithm learning efficiency is high." @default.
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- W4308337575 date "2022-10-01" @default.
- W4308337575 modified "2023-09-30" @default.
- W4308337575 title "Research on virtual entity decision model for LVC tactical confrontation of army units" @default.
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- W4308337575 doi "https://doi.org/10.23919/jsee.2022.000119" @default.
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