# Research on Wargame Decision-Making Method Based on Multi-Agent Deep Deterministic Policy Gradient
###### Abstract
While there has been some success in the studies mentioned above, there are limitations in the application of traditional AI techniques to assist in decision-making regarding wargames. These limitations stem from three major factors: insufficient dynamic adaptation capability, insufficient global capability, and insufficient autonomous decision-making ability. Firstly, traditional AI techniques, such as ant colony algorithms [13], simulated annealing algorithms [14], and other heuristic algorithms, are based on existing problems to find optimal solutions. These generated paradigms or models are very efficient in solving similar scenarios. However, when a more complex scenario or scenario changes, the generated model becomes unusable. This limitation makes it difficult to adapt to the needs of a modern fast-paced society [15]. Secondly, most of the traditional intelligent decision-making technologies are oriented to local decision-making problems in a specific field under a single link or a specific objective, and the local optimal solution is obtained [16]. Traditional intelligent decision-making technologies are incapable of finding the relationships between these fields, and they consequently struggle to provide global decision-making support [17]. Lastly, there is insufficient autonomous decision-making ability. Machine learning technologies, represented by neural networks, have rapidly improved the speed of model training to meet the needs of many decision-making scenarios [18]. For example, traditional intelligent decision-making technologies are often unable to make quick judgments in the face of unprecedented unstructured environments [19]. The aforementioned studies identified limitations in the implementation of traditional AI methods, and to overcome these issues, the researchers explored the introduction of reinforcement learning techniques into wargame decision-making. One such study [20] employed reinforcement learning multi-agent deep deterministic policy gradient algorithms for dynamic decision-making in game AI, while also leveraging deep learning and natural language processing techniques to transform game context maps into textual suggestions during wargame confrontations. By combining reinforcement learning techniques, deep learning techniques, and natural language processing techniques, semantic text with state-of-the-art accuracy output enables generalization, thereby playing a crucial role in enhancing the human understanding of game AI behavior. [PERSON] [21] found that reinforcement learning techniques can be used to predict the action of a given action of a given action. ment learning-based models are more robust and powerful than expert experience-based approaches in a wargame environment, but require more time to train. [PERSON] [22] automated troop deployment in wargames using reinforcement learning. [PERSON] [23] trained AI agents for optimal offensive behavior validated by the tactical principles of mass and economic power. [PERSON] [24] used reinforcement learning to design strategies for wargame simulations and to provide commanders with decision-making support. Nevertheless, despite the significant achievements of these researchers, wargame decision-making models still suffer from large decision space, local optimal solution, and slow training convergence. From the above investigations, it can be seen that although reinforcement learning has made some progress in the field of wargame decision-making, problems such as large decision space, local optimal solution, and slow training convergence still exist, which may reduce the wargame win rates. A wargame decision-making method which optimizes the MADDPG algorithm as well as divides the wargame decision-making method into supervised learning and reinforcement learning phases is proposed to address these challenges, hence enabling a faster training convergence, higher reward values, and achieving the global optimal solution and ultimately improved wargame win rates.