# Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning +
## 1 Introduction  
Personality, which can be defined under characteristic patterns of thought, emotion, and behavior that remain consistent over time and situations [1], plays a key role in shaping behavior, thoughts, and emotions, making it valuable to understand in personal and professional contexts for insights into actions, decisions, and growth. Self-report questionnaires and psychological assessments are common tools for evaluating personality, but they come with limitations, as self-perception biases may affect accuracy. These tools are best complemented by additional feedback or professional evaluations, which can be enhanced by technology [2]. Advancements, especially in gaming, have expanded into fields like education, assessment, and diagnosis. This has led to \"serious games\", which use gaming for practical purposes like skill-building and evaluation. Gaming environments promote exploration, problem-solving, and skill development, enhancing cognitive and soft skills like critical thinking and adaptability, which are valuable in today's workplace [3]. Escape Room (ER) games, a genre that emphasizes teamwork and communication, have emerged as tools in corporate settings for evaluating and fostering these skills. ER games combine physical and mental challenges in dynamic settings, allowing personality traits to manifest naturally and offering a richer understanding than static assessments. However, traditional evaluations of team performance in ER games rely on post-game questionnaires, which can be biased and fail to capture all player actions [4]. A digitalEscape Room game could overcome these limitations, providing a more comprehensive, real-time approach to tracking and evaluating individual and team dynamics [5]. So, emulating human behavior in a gaming environment is a complex undertaking due to the complexity of personality, which makes it challenging to quantify without a large number of distinct human players with specific traits. To develop a new personality assessment method, we can combine a digital, serious Escape Room (ER) game with AI technology. This setup allows the AI to analyze gameplay data, identifying patterns that improve assessment accuracy. The ER game triggers human player behaviors, and an AI-based regression system analyzes the data collected to assess their personality traits following the OCEAN Five model [6], which was chosen due to being the most scientifically robust and widely used model in academic psychology and research. However, training this Multi-Output Regression System (MORS) requires extensive labeled data, which is challenging to obtain from human participants with specific trait assessments. To overcome the challenge of gathering large amounts of labeled personality data, we developed a multi-agent system that can simulate gameplay and generate the necessary data through self-play. Each agent is trained on a specific personality trait, based on custom rewards functions that are based on mathematical formulas from the HiDAC model [7] that represents behaviors. The agents are rewarded not only for solving challenges within the environment but also for displaying behaviors that align with their assigned traits while cooperating. Related simulations and their results can serve as a base and a benchmark for modeling behaviors in other environments. In this study, we used the HiDAC crowd simulation as our baseline, which is frequently utilized as a benchmark for evaluating models of human behavior. To train the agents effectively, we used the MA-POCA (Multi-Agent Posthumous Credit Assignment) [8] reinforcement learning algorithm. This approach enables agents to continue learning even after they complete the environment--in this case, when they \"escape\" the game. This simulation-based training with MA-POCA provides an efficient way to gather high-quality data, refining the MORS's ability to recognize subtle human behavioral patterns and enhancing the overall accuracy of personality assessments within the Escape Room game environment.