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Metrics for Finite Markov Decision Processes
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence, (2012): 950-951
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We present metrics for measuring the similar- ity of states in a finite Markov decision process (MDP). The formulation of our metrics is based on the notion of bisimulation for MDPs, with an aim towards solving discounted infinite horizon reinforcement learning tasks. Such metrics can be used to aggregate states, as well as to bet- ter st...More
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