# Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning +
## 5 Conclusions  
The present study introduced a multi-agent environment in the form of an ER, where agents emulate the OCEAN Five Personality Traits characteristics based on custom reward functions. The environment was designed in a way that permitted us to gather data regarding the play style of the agents, as well as their interactions with the room and between them.  
The RL agents emulate human-like decision-making processes through several technical elements. The game environment is modeled as a high-dimensional space, capturing items, player positions, and interactions. The action space defines how agents can move, pick up objects, solve puzzles, and interact, with each action impacting the game state based on the agent's personality. We implemented a custom reward function designed to balance task-oriented objectives, such as solving puzzles and escaping the room while looking at personality emulation and behaviors. During the training process, the agents are trained using advanced Deep RL algorithms like MA-POCA (Multi-Agent Posthumous Credit Assignment), involving extensive game sessions to improve strategies through trial and error. These elements together ensure that the RL agents provide a comprehensive and dynamic understanding of personality traits within the game.  
In summary, the multi-agent teams developed in this study successfully emulated personality traits and exhibited distinct behaviors while solving tasks within an Escape Room (ER) environment. During the training process, we collected sufficient data to analyze and compare the efficiency and speed of each team based on the personality traits of their respective members. The findings highlight that personality traits significantly influence team dynamics, task-solving efficiency, and the speed at which specific objectives are achieved. These results suggest that our approach can serve as a foundation for developing agents capable of learning to solve tasks in other game environments emulating behaviors, using these or similar reward functions. Furthermore, such agents could also be deployed as Non-Playable Characters (NPCs) capable of exhibiting realistic, personality-driven behaviors, enhancing immersion and player experience in interactive digital environments.  
Moreover, the data generated can be used to train a Multi-Output Regression System (MORS) using supervised machine learning algorithms as future work. This process will use a MORS to assess personality traits by analyzing data and behavioral patterns exhibited during gameplay by human players. Future work will investigate the implementation of a MORS in live, interactive gaming environments, where the system assesses players' behaviors and provides immediate personality insights.  
Conceptualization, G.L. and I.V.; Methodology, G.L. and I.V.; Software, G.L.; Validation, G.L.; Resources, G.L.; Data curation, G.L.; Writing--original draft, G.L.; Writing--review & editing, G.L. and I.V.; Visualization, G.L.; Supervision, I.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
This paper is a revised and expanded version of a paper entitled Machine Learning Methods for Emulating Personality Traits in a Gamified Environment, which was presented at Pireus, Greece, 11-13 September 2024.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
A1 Artificial Intelligence  
E1 Escape Room  
R1 Reinforcement Learning  
MDP Markovian Decision Process  
MA-POCA Multi-Agent Posthumous Credit Assignment  
OCEAN Openness, Consentiousness, Extraversion, Agreeableness, Neuroticism  
MORS Multi-Output Regression System  
MA-A2C Multi-Agent Advantage Actor-Critic