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Metrics for Finite Markov Decision Processes
Norman Ferns, Prakash Panangaden, Doina Precup
We present metrics for measuring the similarity of states in a finite Markov decision process (MDP). The formulation of our metrics is based on the notion of bisimulation for MDPs,…
Metrics for Markov Decision Processes with Infinite State Spaces
Norman Ferns, Prakash Panangaden, Doina Precup
We present metrics for measuring state similarity in Markov decision processes (MDPs) with infinitely many states, including MDPs with continuous state spaces. Such metrics provide…
Methods for computing state similarity in Markov Decision Processes
Norman Ferns, Pablo Samuel Castro, Doina Precup +1
A popular approach to solving large probabilistic systems relies on aggregating states based on a measure of similarity. Many approaches in the literature are heuristic. A number o…
Improved Estimation in Time Varying Models
Doina Precup, Philip Bachman
Locally adapted parameterizations of a model (such as locally weighted regression) are expressive but often suffer from high variance. We describe an approach for reducing the vari…