## 3 Results & Discussion
### Validating the Trained Agents  
The validation of the RL agent involved regularly evaluating the policy it implemented in the actual environment. The results of this process, in which it compared the observed and predicted temperatures for tool and measured driving cycles, are shown in the following figures. Figure 7 presents the outcomes from deploying a TD3 reinforcement learning agent for temperature predictions across three specific driving cycles. This figure shows the temperature predictions over time for the stator and the rotor of an induction motor from tool data. In Figure 7, a blue line represents the actual measured temperature data, and an orange line shows the predicted temperature by the TD3 agent. The close match between measured data and the predictions made by the agents in three DC data indicate that the TD3 agent successfully learned and accurately estimated the thermal dynamics of induction motors. Furthermore, the precision of the temperature prediction demonstrates the agent's capability to generalize well from the training data. Figure 7 also illustrates the performance of a predictive model for DC5 stator and rotor temperatures using the SAC RL agent. TD3 and DDPG agents were employed to get the optimal thermal parameters and temperature predictions from DC5. However, the SAC agent only provided improved temperature predictions for the stator, while it struggles to achieve accurate predictions for the rotor temperature. Figure 7 highlights the fluctuations and spikes in model predictions for stator temperature and indicates the less predictive accuracy of the model in the case of rotor temperature prediction. Optimization is required for DC5 for the thermal management and operational efficiency of electric motors. Rotor temperature discrepancies highlighted in the figures underscore the importance of analyzing thermal management strategies. The rotor, being the rotating part, may experience different levels of heat generation compared to the stator, primarily due to friction, air resistance, and the electrical losses that occur within the rotating component. Furthermore, cooling mechanisms, such as airflow have varying effectiveness on the rotor and stator due to their accessibility. The material properties of the rotor and stator, including thermal conductivity and specific heat capacity, also play significant roles in how heat is generated, absorbed, and transferred. Additionally, operational conditions, such as speed, can further influence the temperature disparity. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Value (DC4, DC6, DC7)** & **Value (DC9)** \\ \hline max episodes & 1000 & 1000 \\ max steps per episodes & 600 & 20 \\ average window length & 500 & 20 \\ stop training value & \(-\)10 & \(-\)740 \\ agent sample time & 0.1 s & 0.1 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training parameters of the DDPG agent for driving cycles DC4, DC6, DC7, and DC9. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Value (Rotor)** & **Value (Stator)** \\ \hline max episodes & 1000 & 1000 \\ max steps per episodes & 600 & 20 \\ average window length & 500 & 50 \\ stop training value & \(-\)10 & \(-\)740 \\ agent sample time & 0.1 s & 0.1 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training parameters of the DDPG agent for driving cycles DC8. Figure 8 displays the stator and rotor temperature predictions of the DDPG reinforcement learning agent on four distinct driving cycles data labeled as cycles DC6, DC7, DC8, and DC9. Figure 8 depicts the DDPG agent's prediction capabilities in aligning its temperature estimation closely with the actual data over time. Additionally, detailed insights highlight that the rotor temperature prediction of DC6 and stator temperature prediction of DC7 were not accurate as still there was room for improvement, and fine-tuning of the deep neural network is required to get approximate predictions from both driving cycles data. Figure 9 depicts the predicted stator winding temperatures from measured data.