Neuroevolution, i.e. evolution of artificial neural networks, has
recently emerged as a powerful technique for solving challenging
reinforcement learning problems. Compared to traditional
(e.g. value-function based) methods, neuroevolution is especially
strong in domains where the state of the world is not fully known: The
state can be disambiguated through recurrency, and novel situations
handled through pattern matching. In this tutorial, I will review (1)
neuroevolution methods that evolve fixed-topology networks, network
topologies, and network construction processes, (2) ways of combining
traditional neural network learning algorithms with evolutionary
methods, and (3) applications of neuroevolution to control, robotics,
artificial life, and games.
A link to the slides is below.
See also the Scholarpedia article on neuroevolution.
To Appear In unpublished. Tutorial slides..