Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Model Minimization in Hierarchical Reinforcement Learning
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
SMDP homomorphisms: an algebraic approach to abstraction in semi-Markov decision processes
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Model minimization in Markov decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Learning to make predictions in partially observable environments without a generative model
Journal of Artificial Intelligence Research
Goal-Directed online learning of predictive models
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
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Most work on Predictive Representations of State (PSRs) focuses on learning a complete model of the system that can be used to answer any question about the future. However, we may be interested only in answering certain kinds of abstract questions. For instance, we may only care about the presence of objects in an image rather than pixel level details. In such cases, we may be able to learn substantially smaller models that answer only such abstract questions. We present the framework of PSR homomorphisms for model abstraction in PSRs. A homomorphism transforms a given PSR into a smaller PSR that provides exact answers to abstract questions in the original PSR. As we shall show, this transformation captures structural and temporal abstractions in the original PSR.