## 3 Results
### Analysis of Methodology Choices
#### 3.5.2 Computation Time  
Our analysis pipeline is computationally efficient. As shown in Table 10, PCA and K-means clustering take only a few seconds to a minute on the subsampled data. The main bottleneck is memory; increasing the number of samples per agent beyond 5000 becomes challenging, highlighting a limitation for even more fine-grained analyses on standard hardware.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**PCA**} & \multicolumn{3}{c}{**K-Means**} \\ \cline{3-5}  & & **50** & **100** & **200** \\ \hline Alien & 12.516 (0.415) & 0.133 (0.007) & 0.546 (0.135) & 0.871 (0.334) \\ Boxing & 12.448 (0.411) & 0.137 (0.006) & 0.445 (0.172) & 0.718 (0.346) \\ Breakout & 13.845 (1.255) & 0.111 (0.006) & 0.443 (0.151) & 0.777 (0.409) \\ Enduro & 12.514 (0.469) & 0.124 (0.003) & 0.537 (0.136) & 0.905 (0.202) \\ Freeway & 12.169 (0.471) & 0.125 (0.004) & 0.465 (0.150) & 0.552 (0.372) \\ KFM & 12.568 (0.509) & 0.119 (0.002) & 0.542 (0.037) & 0.917 (0.254) \\ Pong & 11.910 (0.447) & 0.146 (0.021) & 0.350 (0.178) & 0.403 (0.297) \\ Qbert & 12.170 (0.527) & 0.112 (0.005) & 0.434 (0.145) & 0.676 (0.369) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Mean and standard deviation of computation time (seconds) for PCA and K-means with K = 50, 100, and 200.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**5**} & **10** & **30** & **50** & **200** \\ \hline Alien & 0.9122 & **0.9166** & 0.9092 & 0.9063 & 0.8992 \\ Boxing & 0.9966 & 0.9984 & **0.9985** & 0.9984 & 0.9979 \\ Breakout & 0.9731 & 0.9865 & 0.9893 & 0.9880 & **0.9916** \\ Enduro & 0.9954 & 0.9965 & **0.9969** & 0.9968 & 0.9966 \\ Freeway & 0.9976 & 0.9995 & **0.9998** & 0.9997 & 0.9995 \\ KFM & 0.9958 & **0.9975** & 0.9967 & 0.9963 & 0.9951 \\ Pong & 0.9897 & **0.9959** & **0.9959** & **0.9959** & 0.9946 \\ Qbert & 0.9870 & 0.9994 & **0.9996** & 0.9995 & 0.9993 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Trustworthiness scores of t-SNE embeddings across varying perplexity values. Columns denote different perplexity settings, and cell entries are the corresponding trustworthiness metrics, with the highest score highlighted in bold.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**PCA**} & \multicolumn{3}{c}{**K-Means**} \\ \cline{3-5}  & & **50** & **100** & **200** \\ \hline Alien & 12.516 (0.415) & 0.133 (0.007) & 0.546 (0.135) & 0.871 (0.334) \\ Boxing & 12.448 (0.411) & 0.137 (0.006) & 0.445 (0.172) & 0.718 (0.346) \\ Breakout & 13.845 (1.255) & 0.111 (0.006) & 0.443 (0.151) & 0.777 (0.409) \\ Enduro & 12.514 (0.469) & 0.124 (0.003) & 0.537 (0.136) & 0.905 (0.202) \\ Freeway & 12.169 (0.471) & 0.125 (0.004) & 0.465 (0.150) & 0.552 (0.372) \\ KFM & 12.568 (0.509) & 0.119 (0.002) & 0.542 (0.037) & 0.917 (0.254) \\ Pong & 11.910 (0.447) & 0.146 (0.021) & 0.350 (0.178) & 0.403 (0.297) \\ Qbert & 12.170 (0.527) & 0.112 (0.005) & 0.434 (0.145) & 0.676 (0.369) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Mean and standard deviation of computation time (seconds) for PCA and K-means with K = 50, 100, and 200.