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Using the XCS classifier system for multi-objective reinforcement learning problems (2007)
Journal Article
Studley, M., & Bull, L. (2007). Using the XCS classifier system for multi-objective reinforcement learning problems. Artificial Life, 13(1), 69-86. https://doi.org/10.1162/artl.2007.13.1.69

We investigate the performance of a learning classifier system in some simple multi-objective, multi-step maze problems, using both random and biased action-selection policies for exploration. Results show that the choice of action-selection policy c... Read More about Using the XCS classifier system for multi-objective reinforcement learning problems.

Accuracy-based learning classifier system ensembles with rule-sharing (2007)
Journal Article
Bull, L., Studley, M., Bagnall, A., & Whittley, I. (2007). Accuracy-based learning classifier system ensembles with rule-sharing. IEEE Transactions on Evolutionary Computation, 11(4), 496-502. https://doi.org/10.1109/TEVC.2006.885163

Learning Classifier Systems (LCS) are a method of evolving compact rule-sets using reinforcement learning. This paper presents an investigation into exploiting the population-based nature of LCS for their use within highly-parallel systems, such as... Read More about Accuracy-based learning classifier system ensembles with rule-sharing.

Learning classifier system ensembles with rule-sharing (2007)
Journal Article
Bull, L., Studley, M., Bagnall, A., & Whittley, I. (2007). Learning classifier system ensembles with rule-sharing. IEEE Transactions on Evolutionary Computation, 11(4), 496-502. https://doi.org/10.1109/TEVC.2006.885163

This paper presents an investigation into exploiting the population-based nature of learning classifier systems (LCSs) for their use within highly parallel systems. In particular, the use of simple payoff and accuracy-based LCSs within the ensemble m... Read More about Learning classifier system ensembles with rule-sharing.