## 1 Introduction  
A 2021 study conducted by the US National Renewable Energy Laboratory found that the marine renewable energy sources in the United States have the capacity to meet up to 57% of the nation's energy needs [1]. Alaska alone accounts for approximately 40% of the nation's river and ocean wave resources and up to 90% of the national tidal resources [2]. Despite the abundant energy resources found in our oceans and streams, these resources remain largely untapped. Of the many marine renewable energy technologies being developed, hydrokinetic turbines (HKTs) have been receiving significant attention in the literature. HKTs operate similarly to wind turbines but are located in rivers, tidal inlets, and ocean currents to harness the energy from these resources. Compared to their wind counterparts, HKTs have the added benefit of increased resource predictability, reduced variability, and higher energy density considering the relative density of water to air. However, a high levelized cost of energy (LCOE) is preventing the widespread adoption of HKT technologies. LCOE is loosely defined as the net revenue associated with generating one kilowatt-hour (kWh) of energy, including manufacturing, operations, and other costs. LCOE reductions can be achieved in a number of ways, including turbine design optimization [3]and improved maintenance strategies [4]. In wind turbine systems, the power electronic converter has been found to have a high failure rate, especially in direct-drive technologies [5; 6], directly adding to the maintenance costs of these systems. Thermal cycling at the junction of the device has been identified as a primary cause of device failure [7]. Therefore, a key way to reduce the maintenance costs of HKTs is to reduce the severity of thermal cycling on power electronic devices. There is a large body of research devoted to reducing thermal stress on power electronic devices, termed active thermal control (ATC) [8; 9]. ATC methods have been applied in multiple areas of research, including wind turbine applications. These approaches are generally realized at either the device level, converter level, or system level (including the cooling system) [9; 10]. At the converter level, these efforts to reduce thermal stresses can be built within the high-level control scheme of the system, thereby improving the lifetime of the converter while incurring little to no additional costs. The thermal response of power electronic devices is directly related to the amount of power harvested by the turbine as losses generally increase with power generation. Therefore, it is intuitive to integrate ATC within the control algorithm that is responsible for regulating turbine power, which is often some form of maximum power point tracking (MPPT) control [11; 12]. When utilizing these ATC methods, it is often found that there is a direct trade-off between device lifetime and system performance [8; 9; 13; 14]. Multiple works have been developed to solve this trade-off between device damage and efficiency using an economic-based approach to their ATC methods [10; 15; 16]. Although these works have presented solutions that simultaneously improve the revenue of the turbine system and minimize the damage to power electronic devices, the proposed methods are heavily reliant on models of the turbine. HKT systems have the additional challenge of being much more dependent on the natural environment. For example, biofouling (the growth of marine organisms on the HKT structure) can significantly impact the optimal operating conditions of the HKT by changing the hydrodynamics of the blades [17; 18]. Therefore, the accuracy of the HKT models is not guaranteed. Considering that the dynamics of the HKT system can vary over time, adaptive model-free control methods are increasingly attractive. Reinforcement learning (RL) is a model-free learning-based approach that develops its own understanding of the system by interacting with its environment.