XCSR with computed continuous action

Authors:
Muhammad Iqbal;Will N. Browne;Mengjie Zhang
Affiliations:
Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand;Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand;Evolutionary Computation Research Group, School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
Venue:
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Year:
2012

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Abstract

Wilson extended XCS with interval based conditions to XCSR to handle real valued inputs. However, the possible actions must always be determined in advance. Yet domains such as robot control require numerical actions, so that neither XCS nor XCSR with their discrete actions can yield high performance. In the work presented here, genetic programming-based representation is used for the first time to compute continuous action in XCSR. This XCSR version has been examined on a simple one-dimensional but non-linear testbed problem --- the "frog" problem --- and compared with two continuous action based systems, GCS and XCSFCA. The proposed approach has consistently solved the frog problem and outperformed GCS and XCSFCA.