# Reinforcement Learning Methods for Emulating Personality in a Game Environment +
## 6 Conclusions  
In this work, we introduced a reinforcement learning-based framework that can be used in both single-agent and multi-agent scenarios to model personality-driven behavior in a gamified 3D escape room setting. The objective of our method was to explore the use of custom reward functions derived from the OCEAN Five personality traits model for training agents with distinct behavioral patterns that correspond to specific personality profiles. This framework uses the power of reinforcement learning (RL) to develop agents that can learn complex behaviors that emerge from their personality-aligned incentives instead of being manually scripted. This is achieved through trial-and-error interactions with the environment. We used Raycast, grid-based, and camera sensors--all of which provide varying degrees of environmental awareness--to train agents in the single-agent setup. While Raycast sensors allowed for rapid, reactive navigation with high-resolution, directional input--perfect for navigating obstacles and interacting with locals--grid-based sensors supported structured exploration and long-term planning through a discretized spatial view. Rich visual input from camera sensors enabled more complex, context-aware behaviors; however, because of their intricacy, they took longer to train. These findings demonstrate how an agent's sensory configuration has a significant impact on its learning effectiveness, strategy, and task suitability, with each modality performing best in various situations. To build on this, we extended the system to a multi-agent environment where teams of RL agents, each of whom represented a distinct personality trait, had to cooperate to solve the escape room. The agents' behavior was changed using personality-aligned reward functions, which placed a particular focus on traits like agreeableness and extroversion, which are closely related to communication and teamwork. Through extensive self-play, the agents improved their coordination and learned to adapt their behavior to the roles and styles of their teammates. Our findings demonstrated that team composition had a noticeable impact on overall performance: agreeable agents promoted quicker, better-coordinated teamwork, while introverted agents were competent but completed tasks more slowly. Although mixed-trait teams initially had trouble collaborating, they eventually developed emergent synergies in certain situations. These results support the use of reinforcement learning as a technique for accurately modeling complex social dynamics and personality-driven interaction, in addition to being a tool for optimizing agent behavior to make the personality emulation more realistic [34]. Our experiments showed that agents with richer perceptual input performed noticeably better, especially those that combined visual data, grid sensors, and Raycasts. In contrast to the Raycast-only baseline, which took twice as long (400 k steps) and only achieved 53% success, a Raycast + grid + camera agent converged in just 200 k steps on the basic map, achieving a 73% success rate. This demonstrates how multi-modal sensor fusion can speed up learning and increase task completion rates. Personality-specific team configurations further demonstrated how behavioral traits impact group performance in the multi-agent setup. Due to imbalanced tendencies that make cooperation difficult, uniform teams made up of agents with the same personality frequently performed worse than mixed ones. For instance, a mixed configuration consisting of three introverts and one extrovert achieved 48%, indicating an 18% improvement in success rate compared to the all-default team's 27%. Interestingly, teams with one agreeable agent and three non-agreeable agents had higher rewards (1991) than their inverse (1740), but the latter had a higher success rate, indicating that coordinated team escape is not always the same as maximizing reward.