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ISSN 2083-6473
ISSN 2083-6481 (electronic version)
 

 

 

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Associate Editor
Tomasz Neumann
 

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@am.gdynia.pl
Reinforcement Learning in Ship Handling
1 Gdynia Maritime University, Gdynia, Poland
ABSTRACT: This paper presents the idea of using machine learning techniques to simulate and demonstrate learning behaviour in ship manoeuvring. Simulated model of ship is treated as an agent, which through environmental sensing learns itself to navigate through restricted waters selecting an optimum trajectory. Learning phase of the task is to observe current state and choose one of the available actions. The agent gets positive reward for reaching destination and negative reward for hitting an obstacle. Few reinforcement learning algorithms are considered. Experimental results based on simulation program are presented for different layouts of possible routes within restricted area.
REFERENCES
Eden, T. Knittel, A., Uffelen, R. 2002. Reinforcement Learning: Tutorial
Kaelbling, L.P. & Littman & Moore. 1996. Reinforcement Learning: A Survey
The Reinforcement Learning Repository, University of Massachusetts, Amherst
Sutton, R. 1996. Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding. In Touretzky, D., Mozer, M., & Hasselmo, M. (Eds.), Neural Information Processing Systems 8.
Sutton, R. & Barto, A. 1998. Reinforcement Learning: An Introduction
Tesauro, G. 1995. Temporal Difference Learning and TD- Gammon, Communications of the Association for Computing Machinery, vol. 38, No. 3.
Citation note:
Łącki M.: Reinforcement Learning in Ship Handling. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 2, No. 2, pp. 157-160, 2008

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