Neuro-Evolving Robotic Operatives (NERO) (2007)
Author: Kenneth Stanley
NERO is a machine learning game in which the player uses real-time neuroevolution to train a team of robotic soldiers for combat. Training takes place in a sandbox, where the player can place obstacles and enemies, and change the fitness function according to the skills that should be learned. After training, players can pit their teams against other teams in combat!

Videos of NERO in action are available here:
http://z.cs.utexas.edu/users/nn/nero/video.php

This link is part of the NERO homepage, where you can download the game to try for yourself:
http://nerogame.org/
Kenneth Stanley Postdoc (Alumni) kstanley@cs.ucf.edu
Igor V. Karpov Ph.D. Student ikarpov@cs.utexas.edu
Risto Miikkulainen Professor risto@cs.utexas.edu
Bobby Bryant Ph.D. Student (Alumni) bdbryant@cse.unr.edu
IJCNN-2013 Tutorial on Evolution of Neural Networks 2013
Risto Miikkulainen
Multiagent Learning through Neuroevolution 2012
Risto Miikkulainen, Eliana Feasley, Leif Johnson, Igor Karpov, Padmini Rajagopalan, Aditya Rawal, and Wesley Tansey
Real-Time Evolution of Neural Networks in the NERO Video Game 2006
Kenneth O. Stanley, Bobby D. Bryant, Igor Karpov, Risto Miikkulainen
Real-Time Learning in the NERO Video Game 2005
Kenneth O. Stanley, Ryan Cornelius, Risto Miikkulainen, Thomas D'Silva, and Aliza Gold
Real-time Neuroevolution in the NERO Video Game 2005
Kenneth O. Stanley, Bobby D. Bryant, and Risto Miikkulainen
rtNEAT C++ The rtNEAT package contains source code implementing the real-time NeuroEvolution of Augmenting Topologies method. In ad... 2006

OpenNERO OpenNERO is a general research and education platform for artificial intelligence. The platform is based on a simulatio... 2010