A model for reasoning about persistence and causation
Computational Intelligence
Lookahead planning and latent learning in a classifier system
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Constructive incremental learning from only local information
Neural Computation
Stochastic dynamic programming with factored representations
Artificial Intelligence
The anticipatory classifier system and genetic generalization
Natural Computing: an international journal
Incremental Induction of Decision Trees
Machine Learning
An Algorithmic Description of ACS2
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Incremental learning of linear model trees
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning classifier systems: a survey
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Zcs: A zeroth level classifier system
Evolutionary Computation
Classifier fitness based on accuracy
Evolutionary Computation
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Designing efficient exploration with MACS: modules and function approximation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Considering Unseen States as Impossible in Factored Reinforcement Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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Factored Reinforcement Learning (frl ) is a new technique to solve Factored Markov Decision Problems (fmdp s) when the structure of the problem is not known in advance. Like Anticipatory Learning Classifier Systems (alcs s), it is a model-based Reinforcement Learning approach that includes generalization mechanisms in the presence of a structured domain. In general, frl and alcs s are explicit, state-anticipatory approaches that learn generalized state transition models to improve system behavior based on model-based reinforcement learning techniques. In this contribution, we highlight the conceptual similarities and differences between frl and alcs s, focusing on the one hand on spiti , an instance of frl method, and on alcs s, macs and xacs , on the other hand. Though frl systems seem to benefit from a clearer theoretical grounding, an empirical comparison between spiti and xacs on two benchmark problems reveals that the latter scales much better than the former when some combination of state variables do not occur. Based on this finding, we discuss the mechanisms in xacs that result in the better scalability and propose importing these mechanisms into frl systems.