Intelligent war game deduction method based on reinforcement learning

By constructing a dynamic battlefield model and a dual agent confrontation mechanism, combined with a deep reinforcement learning algorithm, the shortcomings in environmental modeling and strategy generation in existing wargame deductions are solved, and efficient and adaptive tactical decision-making support is achieved.

CN120449646APending Publication Date: 2025-08-08NANJING HANHAI FUXI DEFENSE TECH CO LTD
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Patent Information

Application Number
CN202510461943.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-14
Publication Date
2025-08-08

AI Technical Summary

Technical Problem

The existing wargame deduction technology is difficult to effectively model complex battlefield environments, generate diverse tactical strategies, and simulate real dynamic games through adversarial training, resulting in insufficient authenticity of deduction and decision-making reference value.

Method used

A dynamic battlefield model is built, an intelligent strategy generation algorithm based on deep reinforcement learning is designed, a dual-agent confrontation mechanism is introduced, and the strategies of both red and blue parties are optimized through multiple rounds of alternate training, and the deduction results are displayed in combination with a visual module.

Benefits of technology

It improves the confrontation and authenticity of war chess deduction, generates flexible and adaptive tactical strategies, and improves the efficiency of deduction and the robustness of strategy.

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Abstract

The invention discloses an intelligent war game deduction method based on reinforcement learning, and the method comprises the steps: constructing a dynamic battlefield model: constructing an adjustable battlefield simulation platform, and defining the landform, resources and army distribution elements in a battlefield; a reinforcement learning strategy is generated and optimized, an intelligent strategy generation algorithm based on deep reinforcement learning is designed, and an efficient combat strategy is generated in multiple rounds of training by constructing a state space and an action space and combining a situation reward function; a double-agent chess playing training mechanism is introduced, black parties and white parties are modeled into reinforcement learning agents, red parties and blue parties are modeled into reinforcement learning agents, and a real battlefield game is simulated through multiple rounds of alternate training; result visualization deduction: a dynamic decision visualization function is provided, and battlefield situation, troop dynamics and a strategy execution process can be displayed in real time; according to the method, the problems of rule solidification, limited strategy generation capability, insufficient antagonism and the like in the prior art are solved.
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