## 5 Experiment Analysis
### Experimental Results Analysis
#### 5.2.2 Average Return Analysis  
In the experiment based on average return in this section, we use the same parameter settings and environment as in Section 5.2.1 Win Rate Analysis. Unlike win rate, average return is recorded by tracking the immediate rewards at each time step during each episode of agent training. The cumulative return is then divided by the number of time steps upon reaching a terminal state to obtain the average return. Similar to win rate, average return also reflects changes in the agent's strategy during decision making. When more return is accumulated, it indicates that the agent interacts with the environment for a longer time. However, this does not directly imply a higher win rate, as the agent might exhibit lazy behavior, repeatedly performing actions that yield rewards but are irrelevant to the final objective. Detailed experimental information based on average return can be found in Table 8.  
As shown in Figure 10(a), in the 2s_vs_1 sc environment, there is considerable fluctuation in the algorithm, as indicated by the 2s1 sc row in Table 8. The enhanced DE-COMA algorithm shows an average improvement of 13.29% in fitness-based evaluation criteria compared to the original COMA algorithm. The test results and rankings are shown in Table 9. \begin{table}
\begin{tabular}{c c c} \hline
**Algorithms** & **Average Ranking** & **Final Rank** \\ \hline DE-COMA & 3.52 & 2 \\ COMA & 3.76 & 5 \\ QMIX & 2.43 & 1 \\ VDN & 3.56 & 3 \\ QTRAN & 3.69 & 4 \\ MAVEN & 4.04 & 6 \\ \hline \end{tabular}
\end{table} 
Table 7: Average ranking of DE-COMA, COMA, QMIX, VDN, QTRAN, MAVEN according to the Friedman test on 8m. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Env** & **Results** & **DE-COMA** & **COMA** & **QMIX** & **VDN** & **QTRAN** & **MAVEN** \\ \hline
2s1 sc & Mean & \(1.04\times 10^{01}\) & \(9.18\times 10^{00}\) & \(1.30\times 10^{01}\) & \(\mathbf{1.53\times 10^{01}}\) & \(1.46\times 10^{01}\) & \(1.20\times 10^{01}\) \\  & Std & \(5.06\times 10^{00}\) & \(5.46\times 10^{00}\) & \(5.79\times 10^{00}\) & \(\mathbf{4.80\times 10^{00}}\) & \(5.34\times 10^{00}\) & \(5.39\times 10^{00}\) \\  & Max & \(1.93\times 10^{01}\) & \(1.90\times 10^{01}\) & \(1.95\times 10^{01}\) & \(\mathbf{1.95\times 10^{01}}\) & \(1.95\times 10^{01}\) & \(1.95\times 10^{01}\) \\  & Min & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) \\
2s3z & Mean & \(8.21\times 10^{00}\) & \(3.53\times 10^{00}\) & \(\mathbf{1.48\times 10^{01}}\) & \(1.42\times 10^{01}\) & \(9.32\times 10^{00}\) & \(1.05\times 10^{01}\) \\  & Std & \(3.64\times 10^{00}\) & \(\mathbf{2.18\times 10^{00}}\) & \(4.83\times 10^{00}\) & \(4.46\times 10^{00}\) & \(4.94\times 10^{00}\) & \(5.88\times 10^{00}\) \\  & Max & \(\mathbf{1.99\times 10^{01}}\) & \(1.17\times 10^{01}\) & \(1.98\times 10^{01}\) & \(1.96\times 10^{01}\) & \(1.88\times 10^{01}\) & \(1.89\times 10^{01}\) \\  & Min & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) \\
3m & Mean & \(1.41\times 10^{01}\) & \(1.29\times 10^{01}\) & \(\mathbf{1.46\times 10^{01}}\) & \(1.41\times 10^{01}\) & \(1.44\times 10^{01}\) & \(1.36\times 10^{01}\) \\  & Std & \(3.28\times 10^{00}\) & \(\mathbf{3.25\times 10^{00}}\) & \(5.19\times 10^{00}\) & \(4.68\times 10^{00}\) & \(5.00\times 10^{00}\) & \(6.92\times 10^{00}\) \\  & Max & \(1.95\times 10^{01}\) & \(1.88\times 10^{01}\) & \(1.97\times 10^{01}\) & \(1.97\times 10^{01}\) & \(1.96\times 10^{01}\) & \(\mathbf{1.98\times 10^{01}}\) \\  & Min & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) & \(0.00\times 10^{00}\) \\ \hline \end{tabular}
\end{table} 
Table 8: Average return data for six algorithms on StarCraft II 2s_vs_1 sc, 2s3z, 3m, 8m. In Figure 11b, in the 2s3z environment, the proposed DE-COMA method resolves the poor convergence issue of the COMA algorithm.