This project considers the application of reinforcement learning (RL) to the control of wave energy converters. RL uses experience to map actions to outcomes, and develops a control policy that maximises long-term rewards. By considering the trade-offs between performance and reliability we intend to work towards minimising the levelised cost of energy of wave power.