@article{Lacki_2008,
	author = {Lacki, Miroslaw},
	title = {Reinforcement Learning in Ship Handling},
	journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation},
	volume = {2},
	number = {2},
	pages = {157-160},
	year = {2008},
	url = {./Article_Reinforcement_Learning_in_Ship_Lacki,6,86.html},
	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.},
	issn = {2083-6473},
	publisher = {Gdynia Maritime University, Faculty of Navigation},
	keywords = {Ship Handling, Reinforcement Learning, Machine Learning Techniques, Markov Decision Process (MDP), Multi-Agent Environment, Restricted Waters, Artificial Neural Network (ANN), Manoeuvring}
}