Evolving neural network controllers for unstable systems

The author describes how genetic algorithms (GAs) were used to create recurrent neural networks to control a series of unstable systems. The systems considered are variations of the pole balancing problem: network controllers with two, one, and zero inputs, variable length pole, multiple poles on one cart, and a jointed pole. GAs were able to quickly evolve networks for the one- and two-input pole balancing problems. Networks with zero inputs were only able to valance poles for a few seconds of simulated time due to the network's inability to maintain accurate estimates of their position and pole angle. Also, work in progress on a two-legged walker is briefly described.<<ETX>>