Control of Wave Energy Converters Using Reinforcement Learning

Efficient control of wave energy converters (WECs) is crucial for maximizing energy capture and reducing the Levelized Cost of Energy (LCoE). In this study, we employ a deep reinforcement learning (DRL) framework based on the Soft Actor-Critic (SAC) and Deep Deterministic Policy Gradient (DDPG) algorithms for WEC control. Our approach leverages a novel decoupled co-simulation architecture, training agents episodically in MATLAB to export a robust policy within the WEC-Sim environment. Furthermore, we utilize a rigorous benchmarking protocol to compare the SAC and DDPG agents against a classical Bang-Singular-Bang (BSB) optimal control benchmark. Evaluation under realistic, irregular Pierson-Moskowitz sea states demonstrates that the performance of the RL agents is very close to that of the BSB optimal control baseline. Monte Carlo simulations show that both the DDPG and SAC agents can perform even better than the BSB when the model of the BSB is different from the simulation environment.