Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Compositional Models for Reinforcement Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
CBR for state value function approximation in reinforcement learning
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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Without a model the application of reinforcement learning to control a dynamic system can be hampered by several shortcomings. The number of trials needed to learn a good policy can be costly and time consuming for robotic applications where data is gathered in real-time. In this paper we describe a variable resolution model-based reinforcement learning approach that distributes sample points in the state-space in proportion to the effect of actions. In this way the base learner economises on storage to approximate an effective model. Our approach is conducive to including background knowledge to speed up learning. We show how different types of background knowledge can used to speed up learning in this setting. In particular, we show good performance for a weak type of background knowledge by initially overgeneralising local experience.