# Reinforcement Learning Methods for Emulating Personality in a Game Environment +
Footnote †: This article is a revised and expanded version of a paper published in [PERSON]; [PERSON]; [PERSON] Machine Learning Methods for Emulating Personality Traits in a Gamified Environment. In Proceedings of the 13 th Conference on Artificial Intelligence (SETN 2024), Piraeus, Greece, 11-13 September 2024. [PERSON]  
human resource management. The use of game-based methods to improve learning, engage participants, and meaningfully analyze behaviors is growing [1]. The use of virtual escape rooms (ER), which have become a popular gamified approach that promotes teamwork, communication, and problem-solving under time constraints, is a noteworthy example [2]. Companies now use these settings to evaluate both individual and group performance and foster teamwork. The development of \"Game AI,\" which refers to the application of AI techniques to improve and automate various aspects of video games, coincides with the rise of gamification [3]. By enabling intelligent agent behavior to produce dynamic content and model player behaviors and interactions, artificial intelligence (AI) plays a crucial role in producing immersive experiences. Game AI has changed how games are created and played, moving away from conventional rule-based systems and toward contemporary machine learning algorithms. Creating agents that can play games, analyzing user behavior, and, more recently, debugging and detecting cheating are some of the primary uses of game AI. Personality modeling can be incorporated into these AI systems to produce agents that not only behave intelligently but also display unique behavioral profiles. In our system, we developed a digital escape room (ER) simulation with the goal of collecting and examining the actions of individual agents in order to investigate the possibilities of AI-driven personality modeling in gamified settings. Our framework's first stage focuses on single-agent deep reinforcement learning (DRL), in which each agent is trained to mimic particular personality traits based on the OCEAN Five personality model using a unique reward function. This makes it possible to comprehend in great detail how various traits--like extroversion, agreeableness, or conscientiousness--affect decision-making and problem-solving styles separately. The framework was expanded into a multi-agent system to model team dynamics in a 3D digital escape room after individual behavioral patterns were identified. Each agent still exhibits a unique personality trait in this scenario, but they now work together to solve problems. We are able to investigate both the emergent characteristics of team performance based on different personality combinations as well as the individual efficacy of each agent by using self-play to generate diverse gameplay data. Personality-driven agents created using this framework can be used in a variety of real-world scenarios in addition to analysis and simulation. They can improve immersion and variability in game development by acting as intelligent non-player characters (NPCs) with realistic, trait-based behavior. We utilized another simulator, HiDAC [4], as a basis for our system and more specifically for the behaviors the agents need to emulate. Furthermore, by mimicking various player types, these agents can serve as automated testers or debugging tools that assess system robustness, game balance, and usability under various behavioral scenarios. The contributions of this work are as follows:  
* A scalable framework that supports both single-agent personality modeling and a multi-agent system for analyzing their performance in a gamified escape room environment. * A new reward mechanism that enables deep reinforcement learning agents to emulate distinct human personality traits, grounded in the OCEAN model, through their behavior and decision-making.