# Reinforcement Learning Methods for Emulating Personality in a Game Environment +
## 4 Results
### Singe Agent Results  
The results of the single-agent experiments in a simple and complex environment, as shown in Table 3, demonstrate how sensor configurations have a major influence on task effectiveness and success rate (the escape of the agent). We tested agents with different sensor configurations in our experiments on the simple map. With a mean escape time of 410 s and a 53% success rate, the baseline Raycast agent needed 400,000 training steps. Performance was greatly enhanced by adding a grid sensor: the Raycast + Grid agent converged after 500,000 steps, completed 90% of the trials successfully, and cut the average completion time to 280 s. These findings demonstrate the advantages of spatial context, even in settings that are comparatively simple. Visual \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameters** & **Single-Agent Values** & **Multi-Agent Values** \\ \hline batch size & 128 & 2048 \\ buffer size & 128,000 & 256,000 \\ learning rate & 0.004 & 0.005 \\ hidden units and & 512 & 512 \\ number layers & 2 & 3 \\ epochs & 3 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters and best values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Map** & **Sensors** & **Optimal Steps 1** & **Mean Escape Time** & **Success Rate** \\ \hline \multirow{3}{*}{Simple} & Raycast & 400 k & 410 & 53\% \\ \cline{2-5}  & Raycast + Grid & 500 k & 280 & 90\% \\ \cline{2-5}  & Raycast + Camera & 600 k & 331 & 66\% \\ \cline{2-5}  & Raycast + Grid + Camera & 200 k & 309 & 73\% \\ \hline Complex & Raycast + Grid & 900 k & 320 & 78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Single-agent results effectiveness. input led to further improvements. Despite the increased complexity of visual processing, the Raycast + Camera agent outperformed the Raycast-only baseline, finishing tasks in 331 s with a 66% success rate after 600,000 training steps. The Raycast + Grid + Camera setup produced the best results on the simple map. With only 200,000 steps needed, this agent maintained a 73% success rate, an average escape time of 309 s, and a strong convergence speed-performance balance. These results highlight how crucial multi-sensor fusion is for speeding up learning and enhancing task performance, even in simpler contexts. Combining sensors allows the agent to make more informed decisions by providing it with richer, more accurate information about the surroundings (such as obstacles, exits, and teammates). Raycasts by themselves, for instance, are directional and sparse. A camera provides visual semantics, and the addition of a grid sensor offers spatial awareness (such as an overhead view of adjacent tiles). When combined, they assist the agent in deciphering situations (e.g., where to move, what is an obstacle, and who is a teammate). The agent's state representation is more in line with the actual dynamics of the environment when they have a deeper comprehension of it, which increases the significance and learnability of the reward signal. Therefore, the agent learns more quickly and explores more efficiently with more informative inputs, which directly enhances performance and rewards. It also eliminates the need for the agent to \"guess\" as much. The failure of escape can frequently result from a limited field of view and insufficient environmental feedback in situations with little sensory input, such as Raycast-only agents, which can make it difficult to locate exits precisely. Agents may repeat ineffective behaviors, become stuck in loops, or overlook important paths as a result of inadequate exploration techniques or subpar early learning. Furthermore, conflicting or unclear sensory inputs can cause indecision or delayed actions if they are not properly fused, even when using richer sensors like cameras or grid detectors. These problems may be made worse by structural difficulties in the surroundings, such as blocked exits or labyrinthine layouts.