## 3 Results
### Results of Feature Selection Obtained from the Novel DRL Algorithm  
The novel DRL algorithm described in Section 2 and implemented in MATLAB as shown in Figure 11, was trained for 50 episodes. The episode is the recording of actions, observations, and rewards for the DQN agent during training iteration. The training results offer an average reward of 77.8 as shown in Figure 16. Within the period of iterations, an average reward value above zero means that the agent took correct actions and accumulated 77.8 positive points. Any reward value below zero (negative) means the average action taken by the agent was wrong (recall Section 2.4).  
After the agent had been trained, it was tested to see which feature(s) fulfills the condition in expression Equation (10). Figure 17 shows the plot of the agent's action to select features and observations in terms of RMSE as well as the reward received for each action.  
In Figure 17, it is important to note that there is a time-step difference between the instant when the agent takes actions and when observations are made. The observations are the computations for the RMSE difference based on expression Equation (10). At the start of the simulation, the agent selected feature subset 5, which is a subset of water temperature and voltage [TT105, V], and observed an RMSE difference (gap) of 4.04; hence, it received a reward value of \(-1\). The next action taken by the agent is the selection of feature 1, which is a subset of hydrogen pressure [PT112]. The agent again received a reward of \(-1\) because the RMSE gap was 3.8. However, when the agent selected the feature 2 subset, which is [TT105], it received a reward value of 2 since the RMSE gap was 0.631 and fulfills the condition Equation (10), indicating that it took the best feature. Hence, as shown in Figure 17, the trained DRL agent was able to select feature subset 2, [TT105], which is the cooling water temperature after an initial trial to pick feature subset 5 and 1, which are [PT112, V] and [PT112], respectively.  
**Validation**: To validate the DRL agent's selection, a plot of the Pearson coefficient [27] was made in MATLAB to determine the relationship between the various features obtained from the electrolyser. In Figure 18, each diagonal plot contains the distribution of each variable as a histogram. For example, the first diagonal plot (1,1) contains the histogram distribution of variable [PT112]; the next one (2,2) contains the histogram variable TT105, and so on. Additionally, each off-diagonal plot contains a scatter  
Figure 16: Training process of the DRL agent showing the rewards.  
cated on the left vertical axis with the variable indicated on the horizontal axis, including a pink least-squares reference line. That is, plot (1,2) shows the correlation scatterplot of variable PT112 with variable TT105. On the other hand, the number written in the left upper corner of each plot indicates the correlation coefficient. Thus, it can be seen that the variable TT121 (hydrogen temperature target variable) has the best correlation with stack water temperature [TT105] (correlation coefficient of 0.99). This matches the feature selected by the DRL algorithm.  
Figure 17: Plot from DRL model showing agent actions (in blue), current/previous RMSE (red), and rewards (green).  
Figure 18: Pearson coefficient showing correlation between features (in red dashed lines the highest correlation).