The application of artificial intelligence (AI) in games has been significantly developed and attracted much attention over the past few years. This article not only leverages the reinforcement learning multiagent deep deterministic policy gradient algorithm to realize the dynamic decision-making of game AI but also creatively incorporates deep learning and natural language processing technologies in the wargame field to transform game context situation maps into textual suggestions in wargame confrontation. In this article, we effectively integrate reinforcement learning technologies, deep learning technologies, and natural language processing technologies to generalize the semantic text output at state-of-the-art accuracy, which plays an important role in human understanding of game AI behavior. The experimental results are promising and can be used to verify the feasibility, accuracy, and performance of our proposed model in extensive simulations against benchmarking methods.

With the development of society, intelligent games have gradually become a hot research field. The paper proposes an algorithm that combines the multi-attribute decision-making and reinforcement learning methods to apply to multi-agents' decision-making for wargaming AI (artificial intelligence). This algorithm solves the problem of the agent's low rate of winning against specific rules and its inability to quickly converge during intelligent wargame training. At the same time, a multi-attribute decision-making method based on Entropy-Weight method was proposed to obtain the normalized weighting for each attribute that feeds into a deep reinforcement learning model. A simulation experiment confirms that the RNM-PPO (Real Number Multi-attribute decision-making-PPO) algorithm of multi-attribute decision-making combined with reinforcement learning presented in the paper is significantly more intelligent than the pure reinforcement learning algorithm.

The reinforcement learning problem of complex action control in a multi-player wargame has been a hot research topic in recent years. In this paper, a game system based on turn-based confrontation is designed and implemented with state-of-the-art deep reinforcement learning models. Specifically, we first design a Q-learning algorithm to achieve intelligent decision-making, which is based on the DQN (Deep Q Network) to model complex game behaviors. Then, an a priori knowledge-based algorithm PK-DQN (Prior Knowledge-Deep Q Network) is introduced to improve the DQN algorithm, which accelerates the convergence speed and stability of the algorithm. The experiments demonstrate the correctness of the PK-DQN algorithm, it is validated, and its performance surpasses the conventional DQN algorithm. Furthermore, the PK-DQN algorithm shows effectiveness in defeating the high level of rule-based opponents, which provides promising results for the exploration of the field of smart chess and intelligent game deduction.

Presently, intelligent games have emerged as a substantial research area. Nonetheless, the slow convergence of intelligent wargame training and the low success rates of agents against specific rules present challenges. In this paper, we propose a game confrontation algorithm combining the multiple attribute decision making (MADM) approach from management science and reinforcement learning (RL) technology. This integration enables us to combine the strengths of both approaches and addresses the above issues effectively. This study conducts experiments using the algorithm that integrates MADM and RL techniques to gather confrontation data from the red and blue sides within the Winning-First wargame platform. The data is then analyzed using the weight calculation method of intuitionistic fuzzy numbers (IFNs) to determine each intelligent opponent agent's threat level from the perspective of MADM. The threat level calculated by MADM is used to construct the reward function for the red side. The simulation results demonstrate that the algorithm combining MADM and RL proposed in this study outperforms classical RL algorithms regarding intelligence. This approach effectively addresses issues such as the convergence difficulty caused by random initialization and the sparse rewards for agent neural networks in wargame environments with large maps. Combining the MADM method from management with the RL algorithm in control can lead to cross-disciplinary innovation in academic fields, which provides innovative research values for intelligent wargame design and RL algorithm improvements