Function Approximations for Reinforcement Learning Controller for Wave Energy Converters (Papers Track)

Soumyendu Sarkar (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Alexander Shmakov (UC Irvine); Sahand Ghorbanpour (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Alexandre Pichard (Carnegie Clean Energy); Mathieu Cocho (Carnegie Clean Energy)

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Power & Energy Reinforcement Learning


Waves are a more consistent form of clean energy than wind and solar and the latest Wave Energy Converters (WEC) platforms like CETO 6 have evolved into complex multi-generator designs with a high energy capture potential for financial viability. Multi-Agent Reinforcement Learning (MARL) controller can handle these complexities and control the WEC optimally unlike the default engineering controllers like Spring Damper which suffer from lower energy capture and mechanical stress from the spinning yaw motion. In this paper, we look beyond the normal hyper-parameter and MARL agent tuning and explored the most suitable architecture for the neural network function approximators for the policy and critic networks of MARL which act as its brain. We found that unlike the commonly used fully connected network (FCN) for MARL, the sequential models like transformers and LSTMs can model the WEC system dynamics better. Our novel transformer architecture, Skip Transformer-XL (STrXL), with several gated residual connections in and around the transformer block performed better than the state-of-the-art with faster training convergence. STrXL boosts energy efficiency by an average of 25% to 28% over the existing spring damper (SD) controller for waves at different angles and almost eliminated the mechanical stress from the rotational yaw motion, saving costly maintenance on open seas, and thus reducing the Levelized Cost of wave energy (LCOE). Demo:

Recorded Talk (direct link)