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GECCO
2004
Springer

Gradient-Based Learning Updates Improve XCS Performance in Multistep Problems

9 years 11 months ago
Gradient-Based Learning Updates Improve XCS Performance in Multistep Problems
This paper introduces a gradient-based reward prediction update mechanism to the XCS classifier system as applied in neuralnetwork type learning and function approximation mechanisms. A strong relation of XCS to tabular reinforcement learning and more importantly to neural-based reinforcement learning techniques is drawn. The resulting gradient-based XCS system learns more stable and reliable in previously investigated hard multistep problems. While the investigations are limited to the binary XCS classifier system, the applied gradient-based update mechanism appears also suitable for the real-valued XCS and other learning classifier systems.
Martin V. Butz, David E. Goldberg, Pier Luca Lanzi
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where GECCO
Authors Martin V. Butz, David E. Goldberg, Pier Luca Lanzi
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