# Multi-Agent System for Emulating Personality Traits Using Deep Reinforcement Learning +
## 3 Training Results and Evaluation
### Default Team Rewards  
The results of the best default multi-agent team (without imitating human behavior) are shown in Figure 4. As we can observe, agents learned almost from the start to press buttons and had some positive rewards. In contrast, the team learned to escape and solve the room much later, almost at 3M steps, as can be seen by the first peaks of the green line. This was due to the dynamically changing environment, and this performance was set as a standard for the next agents, which was displayed in every figure in a light and shaded way. We did not intend to compare the agents' to the team rewards, but this is an indication of when training  
Non-neurotic. This way, we could set a baseline of how agents with the same personality traits are performing.  
In all the diagrams of this section, we observe the smoothed and actual rewards of each team at an agent level and at a group level, as well as the default agent reward, to be able to understand how the behaviors change the rewards and effectiveness.  
In Figure 5, we observe the performance of agents with varying levels of Openness. The team with high Openness scored lower rewards compared to the default agents. This difference in performance is primarily due to the high Openness agents' propensity to explore more extensively. Their curiosity and desire for new experiences lead them to spend more time investigating their environment, which, while potentially valuable for gathering information, results in less efficient task completion.  
The exploratory behavior of high-Openness agents can divert attention from immediate goals, causing delays and reducing overall effectiveness. This tendency to prioritize exploration over direct action can lead to lower rewards, as these agents may overlook simpler, more immediate solutions in favor of investigating less obvious possibilities.  
Teams of agents with all the personalities were trained, and the diagrams with complementary analysis can be found in the Appendix A. Table 4 presents a comparison of the teams, summarizing their mean reward, meantime, and success rate to provide an overview of their overall effectiveness.  
We present the Openness agents diagrams as an indicative showcase, and we include the diagrams of all the agents in the Appendix A for clarity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Personality (+/\(-\))** & **Mean Reward 1** & **Mean Escape Time 2** &
\begin{tabular}{c} **Success Rate (All** \\ **Agents escaped)** \\ \end{tabular} \\ \hline Default & 1970 & 410 & 27\% \\ Openness & 1695/2154 & \(505/411\) & 37/20 \% \\ Conscientiousness & 1890/2005 & 446/430 & 29/21\% \\ Extraversion & 1776/1891 & 428/409 & 29/30\% \\ Agreeableness & 1819/2003 & 440/430 & 37/27\% \\ Neuroticism & 2077/1999 & 415/432 & 20/**38**\% \\ \hline \hline \end{tabular}  
* The final reward number based on agent actions. 2 The mean play time in seconds.  
\end{table}
Table 4: Uniform personality teams and effectiveness.  
Figure 5: Openness (O) and Non-Openess (NO) agents and team training results.  
We can observe from the Table 4, the influence of positive and negative traits on the effectiveness of the teams. This showcases just how different the agents behaved in the same environment.