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Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves

Authors :
Sarkar, Soumyendu
Gundecha, Vineet
Ghorbanpour, Sahand
Shmakov, Alexander
Babu, Ashwin Ramesh
Naug, Avisek
Pichard, Alexandre
Cocho, Mathieu
Source :
IJCAI 2023, Proceedings of the Thirty-Second International Joint Conference on Artificial IntelligenceAugust 2023, Article No 688, Pages 6201 to 6209
Publication Year :
2024

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: https://tinyurl.com/yueda3jh<br />Comment: IJCAI 2023, Proceedings of the Thirty-Second International Joint Conference on Artificial IntelligenceAugust 2023

Details

Database :
arXiv
Journal :
IJCAI 2023, Proceedings of the Thirty-Second International Joint Conference on Artificial IntelligenceAugust 2023, Article No 688, Pages 6201 to 6209
Publication Type :
Report
Accession number :
edsarx.2404.10991
Document Type :
Working Paper
Full Text :
https://doi.org/10.24963/ijcai.2023/688