Deep Reinforcement Learning Proposed to Enhance Game AI for Immersive Experiences.

The immersion and engagement in video games are significantly influenced by the quality of in-game characters, or game AI. Traditional hand-coded systems often struggle to capture the necessary behavioral complexity, leading to predictable or unrealistic character actions that can break the illusion of realism. The emergence of machine learning, particularly deep reinforcement learning (DRL), offers a promising avenue to create more authentic and relatable game characters that can learn from game interactions or player data. This paper explores the potential for more widespread application of DRL in game AI. It acknowledges that current research limitations hinder broad deployment across various game genres. To overcome these hurdles, the authors propose a structured framework for training DRL models, specifically designed with the unique requirements of game AI and game development in mind. The research provides examples of games already augmented with DRL-driven AI and discusses the practical considerations for deploying player-facing machine learning agents in modern games. Furthermore, it identifies critical bottlenecks and challenging problems within this domain, outlining promising research directions that could accelerate the adoption of machine learning techniques in the video game industry.