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Speciation of Population in Neuroevolutionary Ship Handling

Lacki M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2010

Vol. 4, no. 2

211--216

This paper presents the idea of using machine learning techniques to simulate and demonstrate learning behavior in ship maneuvering. Simulated helmsman is treated as an individual in population, which through environmental sensing learns itself to navigate through restricted waters selecting an optimum trajectory. Learning phase of the task is to observe current situation and choose one of the available actions. The individual improves his fitness function with reaching destination and decreases its value for hitting an obstacle. Neuroevolutionary approach is used to solve this task. Speciation of population is proposed as a method to secure innovative solutions.

Solving Multi-Ship Encounter Situations by Evolutionary Sets of Cooperating Trajectories

Szlapczynski R.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2010

Vol. 4, no. 2

185--190

The paper introduces a new approach to solving multi-ship encounter situations by combining some of the assumptions of game theory with evolutionary programming techniques. A multi-ship encounter is here modelled as a game played by “thinking players” – the ships of different and possibly changing strate-gies. The solution – an optimal set of cooperating (non-colliding) trajectories is then found by means of evo-lutionary algorithms. The paper contains the description of the problem formulation as well as the details of the evolutionary program. The method can be used for both open waters and restricted water regions.

Reinforcement Learning in Ship Handling

Łącki M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2008

Vol. 2, no. 2

157--160

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.