In recent years, as deep reinforcement learning has reached human level in perception fields such as image speech recognition and natural language processing, people have begun to turn their attention to intelligent decision-making technology that focuses on cognitive decision-making. Since 2015, intelligent decision-making technology has been used in Go, Texas A series of breakthroughs have been made in games such as poker and StarCraft, as well as practical applications in areas such as drone control, autonomous driving, and robot cooperation. The application of relevant intelligent game technologies to wargames can not only effectively accelerate the military decision-making cycle, but also use different types of scenarios in wargames to study intelligent game technologies, which makes wargames easier to use. The research of intelligent game technology in deduction has become a hot issue at present.
Due to the variety of battlefield environments, there are also various types of maps for wargaming deduction. Therefore, research on wargaming is often based on a scenario composed of a specific terrain and a fixed type and number of operators (operable units).This paper first introduces the distributed parallel reinforcement learning training platform for wargaming built from the perspective of data flow, and demonstrates the distributed acceleration technology explored on this basis, and then introduces the intelligence for wargaming from simple to complex research on this basis. Decision-making techniques work. This paper first introduces the reasoning analysis method and the approximate theoretical solution of wargames in the medium fluctuation scenario of operator isomorphism, then proposes a deep reinforcement learning algorithm based on self-game, and finally introduces the experimental verification results in the built platform. . This paper first introduces the process of knowledge analysis and modeling, and the construction of knowledge AI in the heterogeneous operator-heterogeneous water network and rice field scenario, and then demonstrates the staged hybrid-driven deep reinforcement learning training in the platform built on this basis. process and results.
The main contributions of this paper are two points. The first point is to build a distributed parallel reinforcement learning training platform for wargames. The platform realizes features such as program parallelism and data flow optimization, thereby increasing the throughput and speeding up the data processing process. As a result, the training process of deep reinforcement learning can be significantly accelerated. The second point is to propose an intelligent decision-making method for wargames. In the simple scenario of operator isomorphism, the intelligence level of the agent can be improved through the improved self-game algorithm. In the complex scenario of operator heterogeneity, it can be divided into The hybrid-driven reinforcement learning algorithm of the stage realizes the improvement of the decision-making level of the agent. Based on the above training platform and algorithm innovation, the hybrid drive algorithm we proposed won the fourth place in the rematch in the Tencent "Kaiwu" King of Glory Invitational Tournament, and won the third place in the first national air game competition.
|IS Representative Paper||是|
|Sub direction classification||机器学习|
|planning direction of the national heavy laboratory||人-机-算法混合与协同决策|
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