TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Classifiers that approximate functions
Natural Computing: an international journal
A Randomized ANOVA Procedure for Comparing Performance Curves
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Extending XCSF beyond linear approximation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
XCS with computed prediction in multistep environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Function approximation via tile coding: automating parameter choice
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Empirical analysis of generalization and learning in XCS with gradient descent
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolving prediction weights using evolution strategy
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Evolving neural networks for fractured domains
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Fuzzy CMAC with automatic state partition for reinforcementlearning
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Hi-index | 0.00 |
This paper introduces XCSF extended with tile coding prediction: each classifier implements a tile coding approximator; the genetic algorithm is used to adapt both classifier conditions (i.e., to partition the problem) and the parameters of each approximator; thus XCSF evolves an ensemble of tile coding approximators instead of the typical monolithic approximator used in reinforcement learning. The paper reports a comparison between (i) XCSF with tile coding prediction and (ii) plain tile coding. The results show that XCSF with tile coding always reaches optimal performance, it usually learns as fast as the best parametrized tile coding, and it can be faster than the typical tile coding setting. In addition, the analysis of the evolved tile coding ensembles shows that XCSF actually adapts local approximators following what is currently considered the best strategy to adapt the tile coding parameters in a given problem.