# Reinforcement Learning Methods for Emulating Personality in a Game Environment +
## 4 Results
### Multi-Agent Results  
Table 4 summarizes the results of our evaluation of different agent team configurations for the multi-agent environment. First, we trained teams of only agents with and without openness, as well as a default team of agents with no particular personality traits. These were used as baselines to determine reference points for subsequent personality-based assessments as well as to evaluate the relative effectiveness of our training methods. We then trained teams to display particular personality traits, emphasizing extroverisation and agreeableness. These characteristics were specifically picked because they are highly relevant to collaborative dynamics and team-oriented behaviors, which makes them especially useful for examining multi-agent cooperation. Lastly, we trained teams of agents with a variety of personality types, including extroverted, introverted, agreeable, and non-agreeable agents, to better represent real-world personality distributions, such as a 25% introvert to 75% extrovert ratio. This gave us the opportunity to look into how various trait combinations affect the dynamics and performance of the team as a whole. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**id** & **Personality (+/\(-\))** & **Mean Reward 1** & **Mean Escape Time 2** &
\begin{tabular}{c} **Success Rate** \\ **(All Agents Escaped)** \\ \end{tabular} \\ \hline
1 & Default & 1970 & 410 & 27\% \\
2 & Openness & 1695/2154 & 505/411 & 37/20\% \\
3 & Extraversion & 1776/1891 & 428/409 & 29/30\% \\
4 & Agreeableness & 1819/2003 & 440/430 & 37/27\% \\ \hline
5 & 3 Extroverts and 1 Introvert & 1150 & 380 & 39\% (+10\%) \\ \hline
6 & 3 Introverts + 1 Extrovert & 1318 & 403 & 48\% (+18\%) \\
7 & 3 Agreeableness and & 1740 & 435 & 35\% (\(-2\%\)) \\  & 1 Non Agreeableness & 1991 & 445 & 30\% (+2\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Uniform personality teams and effectiveness. Figure 4 shows the results of the optimal default multi-agent team (without simulating behaviors). The majority of the time, it learns to solve the room, but occasionally, as the reward drops show, some agents were unable to escape. This is related to the ever-changing environment. When compared to the default agents, the team with the highest openness scores lower rewards, as seen in Table 4. The main cause of this performance discrepancy is the high openness agents' inclination to conduct more thorough exploration. Their inside curiosity and yearning for novel experiences cause them to spend more time exploring their surroundings, which may be beneficial for learning but leads to less effective task completion. Both the extrovert-only (orange) and introverted (pink) teams, as shown in Figure 5, learn to solve the room rather quickly, but they have drops of rewards (i.e., not all agents were able to escape). Their sometimes impatient behavior and poor cooperation may be the cause of this. Given their average episode duration, we must observe that introverts (pink) take longer to complete the room because, according to the bibliography, they tend to move more slowly (Figure 6). While the non-agreeable agents (cyan) were a little less efficient but learned to solve the room a little earlier, they showed some fluctuation of results related to their impatience. In Figure 7, the team with only agreeable (red) agents takes some time to learn to solve the room but has some drops of rewards. We must also highlight that teams of agreeable (red) and non-agreeable agents (cyan) take almost the same amount of time to solve the room, with a fluctuation after a while, as illustrated in Figure 8. This seems to happen because their actions are associated with patience rather than speed. Figure 4: Default (without simulating behaviors) multi-agent team. Figure 5: Team rewards for introverts (pink) and extroverts (orange). Figure 6: Length of episode for introvert (pink) and extrovert (orange).