The success of evolutionary methods on standard control learning tasks has created a need for new benchmarks. The classic pole balancing problem is no longer difficult enough to serve as a viable yardstick for measuring the learning efficiency of these systems. In this paper we present a more difficult version to the classic problem where the cart and pole can move in a plane. We demonstrate a neuroevolution system (Enforced Sub-Populations, or ESP) that can solve this difficult problem without velocity information.

PDF, PS

In Proceedings of the International Conference on Artificial Neural Networks (ICANN-98), 425-430, Skovde, Sweden, 1998. Berlin, New York: Springer.

@InProceedings{gomez:icann98, title={2-D Pole Balancing With Recurrent Evolutionary Networks}, author={Faustino Gomez and Risto Miikkulainen}, booktitle={Proceedings of the International Conference on Artificial Neural Networks (ICANN-98)}, address={Skovde, Sweden}, publisher={Berlin, New York: Springer}, pages={425-430}, url="http://nn.cs.utexas.edu/?gomez:icann98", year={1998} }