Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves
arxiv(2024)
摘要
The industrial multi-generator Wave Energy Converters (WEC) must handle
multiple simultaneous waves coming from different directions called spread
waves. These complex devices in challenging circumstances need controllers with
multiple objectives of energy capture efficiency, reduction of structural
stress to limit maintenance, and proactive protection against high waves. The
Multi-Agent Reinforcement Learning (MARL) controller trained with the Proximal
Policy Optimization (PPO) algorithm can handle these complexities. In this
paper, we explore different function approximations for the policy and critic
networks in modeling the sequential nature of the system dynamics and find that
they are key to better performance. We investigated the performance of a fully
connected neural network (FCN), LSTM, and Transformer model variants with
varying depths and gated residual connections. Our results show that the
transformer model of moderate depth with gated residual connections around the
multi-head attention, multi-layer perceptron, and the transformer block (STrXL)
proposed in this paper is optimal and boosts energy efficiency by an average of
22.1
controller. Furthermore, unlike the default SD controller, the transformer
controller almost eliminated the mechanical stress from the rotational yaw
motion for angled waves. Demo: https://tinyurl.com/yueda3jh
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要