Enhancing Non-Player Characters (NPC) Behaviour in Video Games Using Reinforcement Learning

Abstract

NPCs enrich the immersive experience of a video game, and traditionally exist along purely rule- or script-based paradigms, denying adaptability or intelligent decision-making very often. The research integrates RL into the NPC behaviour to allow for the more realistic, dynamic interactions and responsive behaviour that today's gaming environments require. We will review state-of-the-art RL algorithms and validate improvements implemented in our own RL model within a sandbox game environment into NPC decision-making and player engagement. According to our results, RL makes NPCs adaptive, tactically deep, and realistic while the classical ones fail. The study provides rigorous methodology and analysis to demonstrate the feasibility and advantages of using RL for the design of a new generation of games.