Skip Training for Multi-Agent Reinforcement Learning Controller for
Industrial Wave Energy Converters
- URL: http://arxiv.org/abs/2209.05656v1
- Date: Tue, 13 Sep 2022 00:20:31 GMT
- Title: Skip Training for Multi-Agent Reinforcement Learning Controller for
Industrial Wave Energy Converters
- Authors: Soumyendu Sarkar, Vineet Gundecha, Sahand Ghorbanpour, Alexander
Shmakov, Ashwin Ramesh Babu, Alexandre Pichard, and Mathieu Cocho
- Abstract summary: Recent Wave Energy Converters (WEC) are equipped with multiple legs and generators to maximize energy generation.
Traditional controllers have shown limitations to capture complex wave patterns and the controllers must efficiently maximize the energy capture.
This paper introduces a Multi-Agent Reinforcement Learning controller (MARL), which outperforms the traditionally used spring damper controller.
- Score: 94.84709449845352
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent Wave Energy Converters (WEC) are equipped with multiple legs and
generators to maximize energy generation. Traditional controllers have shown
limitations to capture complex wave patterns and the controllers must
efficiently maximize the energy capture. This paper introduces a Multi-Agent
Reinforcement Learning controller (MARL), which outperforms the traditionally
used spring damper controller. Our initial studies show that the complex nature
of problems makes it hard for training to converge. Hence, we propose a novel
skip training approach which enables the MARL training to overcome performance
saturation and converge to more optimum controllers compared to default MARL
training, boosting power generation. We also present another novel hybrid
training initialization (STHTI) approach, where the individual agents of the
MARL controllers can be initially trained against the baseline Spring Damper
(SD) controller individually and then be trained one agent at a time or all
together in future iterations to accelerate convergence. We achieved
double-digit gains in energy efficiency over the baseline Spring Damper
controller with the proposed MARL controllers using the Asynchronous Advantage
Actor-Critic (A3C) algorithm.
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