Game AI Training Based on Reinforcement Learning and Deep Reinforcement Learning

Abstract

This article explores the importance of game AI training as an important area of cross fusion between computer science and artificial intelligence, particularly in the core position of reinforcement learning research. Game AI training is not only a hot topic in technical practice, but also a key carrier environment for promoting innovation in artificial intelligence theory and methods. Currently, in the practical process of game AI training, we are facing many challenges, including the dual pressure of ethical considerations and technological innovation. These challenges require us to delve into key technical issues in game AI training, such as precise analysis of coefficients and delayed feedback, effective exploration of high-dimensional states and complex action spaces, and improving the robustness of strategy learning in unstable environments. In response to these challenges, this article proposes an innovative solution based on the latest developments in deep reinforcement learning. By integrating the advantages of reinforcement learning and deep learning, we have constructed a basic framework for deep reinforcement learning based on attention mechanism. This framework aims to solve the problem of cluster intelligence in complex environments, by intelligently allocating attention resources to improve the decision-making efficiency and accuracy of AI systems in processing massive amounts of information and dynamic environments. This study not only provides a new technological path for game AI training, but also provides theoretical support and practical guidance for the application of artificial intelligence in a wider range of fields.