Intelligent Decision-Making and Human Language Communication Based on Deep Reinforcement Learning in a Wargame Environment
Yuxiang Sun, Bo Yuan, Qi Xiang, Jiawei Zhou, Jiahui Yu, Di Dai, Xianzhong Zhou
Abstract
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.