## 5 Discussion  
### Performance  
During the training protocol, the RL agent converged to a policy within 15 epochs, which led to an improvement in terms of reward for all four training years. The results for 2023 found the largest benefit during the proposed training, with significantly higher increases in rewards and a much more consistent reward profile. The RL agent's strong performance during the 2023 training sessions is just one example of how the cost of energy or the LMP profile influences its learning ability. As shown, the reward impacts are directly related to the average cost of energy each year: the lower the energy costs, the lower the total reward accumulated by the RL agent.  
For the case study, the focal point was comparing the life consumption and the total annual energy generation between the RL-based method and the baseline controller for the two mission profiles. A summary of the case study results is shown in Table 5, where LC Impact, Energy Impact, and Revenue Impact are defined as the relative differences between the values calculated for the RL-based control scheme and the baseline method normalized by the values achieved by the baseline method. In Table 5, a negative sign represents a relative decrease and a positive sign represents a relative increase in the respective category. It should be noted that the estimates for revenue have been calculated as the difference in reward signals \(r_{g}\) and \(r_{c}\), which are defined in (23) and (24), respectively.  
While it has been shown that the RL method leads to significant reductions in \(LC\) in both case studies, the impacts on energy generation are non-negligible, especially in 2024. The Revenue Impact category provides more insight as to how the system costs will be impacted under the proposed RL method. These results show that, despite the 25% reduction in energy generation in 2024, the estimated revenue increases by over 45%  
Figure 14: Energy harvested by the turbine (\(E_{turbine}\)) using both the baseline and RL-based control schemes for the (**a**) 2019 case study and (**b**) 2024 case study.  
when the RL method is in use. On the other hand, it was found that, in 2019, there was a reduction in estimated income of 1.3% under the proposed control method. These impacts show how crucial the LMP profile is in determining how useful the proposed RL method is. Further, 2019 consistently has higher energy costs than in 2024, as shown in Figure 10. With these higher energy costs, the potential benefits of extending the lifetime of power electronics do not outweigh the relative revenue coming in from the generation of energy. On the other hand, in 2024, when the energy costs are significantly lower with short-term spikes, the RL method excels because the relative cost of the power electronic converter is on the same order of magnitude as the price of energy.  
In summary, as shown by the results of these two case studies, the potential improvements in the RL-based control system in tidal HKT applications are highly dependent on the current energy market. In areas where the price of energy is high, the benefits in extending the lifetime of the power electronic converter do not outweigh the loss of revenue from reduced HKT performance. On the other hand, in areas where the energy market covers a wider range of energy costs, the proposed RL method has shown to lead to significant increases in estimated net income.