Learning Monitoring Strategies to Compensate for Model Uncertainty

Eric A. Hansen and Paul R. Cohen

This paper addresses the need for monitoring the environment given an action model that is uncertain or stochastic. Its contribution is to describe how monitoring costs can be included in the framework of Markov decision problems. making it possible to acquire cost-effective monitoring strategies using dynamic pmgnmuning or related reinforcement learning algorithms.


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