Abstract
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% for these complex spread waves over the existing spring damper (SD) controller. Furthermore, unlike the default SD controller, the transformer controller almost eliminated the mechanical stress from the rotational yaw motion for angled waves. Demo: this https URL
Abstract (translated)
用于工业多功能发电机（WEC）的多功能波浪能量转换器（WEC）必须处理来自不同方向的多重同时波浪，这些具有复杂情况的设备需要具有多重能源捕捉效率、降低结构应力以限制维护和主动抗高波浪保护的控制器。使用Proximal Policy Optimization（PPO）算法进行多智能体强化学习（MARL）控制器可以处理这些复杂性。在本文中，我们探讨了用于建模系统动态序列的不同功能逼近对策略和评分网络的影响，并发现它们对系统性能至关重要。我们研究了具有不同深度的全连接神经网络（FCN）、LSTM和Transformer模型变体，并发现Transformer模型具有围绕多头注意、多层感知器和Transformer模块（STrXL）的软开环连接，对于这些复杂扩散波浪具有最优性能，并提高了约22.1%的能源效率。此外，与默认的SD控制器相比，Transformer控制器几乎消除了角波上的机械应力。演示：此链接
URL
https://arxiv.org/abs/2404.10991
