Abstract
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.
This research was supported in part by National Science Foundation under grant #IRI-9504317.
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© 1998 Springer-Verlag London
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Gomez, F., Miikkulainen, R. (1998). 2-D Pole Balancing with Recurrent Evolutionary Networks. In: Niklasson, L., Bodén, M., Ziemke, T. (eds) ICANN 98. ICANN 1998. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1599-1_63
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DOI: https://doi.org/10.1007/978-1-4471-1599-1_63
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