Work

Discovering Control Scheduler Policies Through Reinforcement Learning and Evolutionary Strategies

Year: 2025

Type: article

Abstract: This work investigates the viability of using NNs to select an appropriate controller for a dynamic system based on its current state. To this end, this work proposes a method for training a controller-scheduling policy using several learning algorithms, including deep reinforcement learning and evolutionary strategies. The performance of these scheduler-based approaches is evaluated on an inverted pendulum, and the results are compared with those of NNs that operate directly in a continuous action space and a backpropagation-based Control Scheduling Neural Network. The results demonstrate that machine learning can successfully train a policy to choose the correct controller. The findings highlight that evolutionary strategies offer a compelling trade-off between final performance and computational time, making them an efficient alternative among the scheduling methods tested.

Source: Actuators

DOI: 10.3390/act14120604

Authors: Aureo Guilherme Dobrikopf, Gabriel Abatti, Douglas Wildgrube Bertol

Institution: Universidade do Estado de Santa Catarina

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina