Optimal control of ship unloaders using reinforcement learning

Autor(es)

Scardua‚ L.A. ; Da Cruz‚ J.J. ; Reali Costa‚ A.H.

• Tipo de documento: Artigo Científico
• Fonte: ADVANCED ENGINEERING INFORMATICS; v. 16, n. 3, p. 217-227, 2002.

Resumo

This paper describes the use of Reinforcement Learning (RL) to the computation of time-optimal anti-swing control of a ship unloader. The unloading cycle has been divided into six subtasks and an optimization problem has been defined for each of them. A RL algorithm together with a multilayer perceptron neural network as a value function approximator have been used in the optimization. The results obtained are encouraging, since they reproduce a solution previously generated by using Optimal Control Theory. (C) 2003 Elsevier Science Ltd. All rights reserved. (AU)

• Processo FAPESP: 97/04668-1 - Controle de sistemas com dinâmica sujeita a incertezas
• Beneficiário: Oswaldo Luiz Do Valle Costa
• Modalidade de apoio: Auxílio à Pesquisa - Temático