Neuroevolution in games

[Julian Togelius]

Neuroevolution - the evolution of weights and/or topology for neural
networks - is a common and powerful method in evolutionary robotics
and machine learning. In the last decade or so, we have seen a large
number of applications of neuroevolution in games. Evolved neural
networks have been used to play games, model players, generate content
and even enable completely new game genres. To some extent, games seem
to be replacing the small mobile robots ubiquitous in evolutionary
robotics and simple benchmarks used in reinforcement learning
research.

Sebastian Risi and I have written a survey on neuroevolution in
games, including a discussion of future research challenges. The main
reason is that there was no survey of neuroevolution in games in
existence; the other reason was that we wanted a tutorial overview to
hand out to the students in our Modern AI for Games course.

A while back we asked the community to send us comments and
suggestions for important work we might have overlooked. We received a
lot of useful input and incorporated most of the suggested work. Thank
you for very much for the help!

Now we are happy to announce that the paper will finally be published
in the IEEE Transactions on Computational Intelligence and AI in Games
(TCIAIG). We hope you like it, and hope it spurs even more comments
and discussion!

TCIAIG early access:
http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7307180

A preprint of the manuscript is available here:
http://arxiv.org/pdf/1410.7326v3.pdf

--
Julian Togelius
Associate Professor, New York University
Department of Computer Science and Engineering
mail: jul...@togelius.com, web: http://julian.togelius.com