- Tytuł:
- Reinforcement Learning in Ship Handling
- Autorzy:
- Łącki, M.
- Powiązania:
- https://bibliotekanauki.pl/articles/117361.pdf
- Data publikacji:
- 2008
- Wydawca:
- Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
- Tematy:
-
Ship Handling
Reinforcement Learning
Machine Learning Techniques
Manoeuvring
Restricted Waters
Markov Decision Process (MDP)
Artificial Neural Network (ANN)
multi-agent environment - Opis:
- 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.
- Źródło:
-
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2008, 2, 2; 157-160
2083-6473
2083-6481 - Pojawia się w:
- TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
- Dostawca treści:
- Biblioteka Nauki
Artykuł