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We evolve a neural network controller for a boat that learns to maintain a given bearing and range with respect to a moving target in the Lagoon 3D game environment. Simulating realistic physics makes maneuvering boats difficult and thus makes an evolutionary approach an attractive alternative to hand coded methods. We evolve the weights of simple recurrent neural networks trained with a fitness function designed to combine multiple fitness objectives based on speed, heading, and position to create a robust maintain station behavior. Results with an enforced subpopulation neural-evolution genetic algorithm indicate that we can consistently evolve robust maintain controllers for realistically simulated boats in Lagoon.


Nathan A. Penrod, David Carr, Sushil Louis, and Bobby D. Bryant (2008).
Neuro-evolving Maintain-Station Behavior for Realistically Simulated Boats.
''Proceedings of the 2008 Congress on Evolutionary Computation (CEC 2008)'', pp. 3326-3332. Piscataway, NJ: IEEE Pres.

* Self-archived [http://nebl.cse.unr.edu/archive/papers/penrod-2008-cec.pdf PDF]

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