## 5 Discussion
### Implications and Future Work  
First, to use the RBF-based approximation method, the designer must designate a large enough state space that includes all the conditions the RL agent may experience. If the RL agent experiences states outside of what has been defined, the learned Q-surface may be heavily skewed to approximate the Q-value of these outlier states, resulting in poor performance within the defined state space. Considering the high variability in LMP data, it may be significantly challenging to decipher the total range that the LMP data may cover while the RL agent is online. More extensive datasets with various LMP profiles would provide the designer more insight into what the landscape for the energy market may look like for a given year, but even possessing extensive historical data may not be sufficient to estimate what might happen in the future. Therefore, it may be beneficial to explore other function approximation methods that do not require explicit bounds on the allowable state space. Second, the deployed environment for the HKT can significantly impact the lifetime of the power electronic converter. For example, in riverine environments, the fluctuations in flow velocity are of much lower frequency, typically on the order of months. In contrast, the flow velocity undergoes multiple cycles per day for tidal applications, as presented in this work. As flow conditions play a major role in the thermal cycling of the device, the proposed RL method would likely have less of a positive impact on the lifetime of the converter in riverine applications. A side-by-side comparison of the relative impacts on \(LC\) and \(E_{turbine}\) should be carried out for both tidal and riverine applications to more broadly generalize how well the proposed RL-based control scheme performs in these two cases. Third, several electrical characteristics were simplified in this work, such as generator efficiency, field weakening effects, and switching/fundamental frequency effects on the thermal response of the power electronics. These characteristics may have a significant impact on the cumulative energy generation of the HKT, as well as the device losses. For example, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **LC Impact** & **Energy Impact** & **Revenue Impact** \\ \hline
2019 & \(-\)76.7\% & \(-\)5.8\% & \(-\)1.3\% \\
2024 & \(-\)84.5\% & \(-\)25.2\% & +45.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A summary of the impacts on life consumption, energy generation, and estimated revenue comparing the proposed RL method to the baseline static k-omega-squared controller. the implementation of the RL method may lead to an increased number of fundamental frequency cycles as the speed of the turbine is higher under this control scheme, which may reduce the overall \(LC\) impact realized through the case study. In future works, these effects could be realized by utilizing a multi-physics modeling scheme such as the HKT-specific method proposed in [34]. After the RL agent has been trained on the high-level HKT model, the switching-level effects and generator impacts can be analyzed to present a more realistic picture of the device's thermal response and overall energy generation. Fourth, this work only considered the impacts of \(\Delta T_{j}\) and \(T_{min}\) on the life consumption of the device. As noted before, there are several other more complex lifetime consumption estimation models, but, without accurate scaling terms, the estimates for \(LC\) are significantly devalued. Although there are a number of resources for IGBT devices, little to no data have been published for SiC MOSFETs. Future work should aim to either complete the required lifetime tests and derive the pertinent scaling terms or partner with a SiC device manufacturer willing to share these values. Lastly, as mentioned earlier, deploying RL in continuous tasks makes the agent susceptible to forgetfulness. In serious cases, forgetfulness can lead to the agent becoming untrained over time.