Batch Reinforcement Learning of Feasible Trajectories in a Ship Maneuvering Simulator

  • José Amendola USP
  • Eduardo A. Tannuri USP
  • Fabio G. Cozman USP
  • Anna H. Reali USP


Ship control in port channels is a challenging problem that has resisted automated solutions. In this paper we focus on reinforcement learning of control signals so as to steer ships in their maneuvers. The learning process uses fitted Q iteration together with a Ship Maneuvering Simulator. Domain knowledge is used to develop a compact state-space model; we show how this model and the learning process lead to ship maneuvering under difficult conditions.


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AMENDOLA, José; TANNURI, Eduardo A.; COZMAN, Fabio G.; REALI, Anna H.. Batch Reinforcement Learning of Feasible Trajectories in a Ship Maneuvering Simulator. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 263-274. ISSN 2763-9061. DOI: