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Abstract : Bisimulation is a notion of behavioural equivalence on the states
of a transition system. Its definition has been extended to Markov decision
processes, where it can be used to aggregate states. A bisimulation metric
is a quantitative analog of bisimulation that measures how similar states
are from a the perspective of long-term behavior. Bisimulation metrics have
been used to establish approximation bounds for state aggregation and other
forms of value function approximation. In this paper, we prove that a
bisimulation metric defined on the state space of a Markov decision process
is the optimal value function of an optimal coupling of two copies of the
original model. We prove the result in the general case of continuous state
spaces. This result has important implications in understanding the
complexity of computing such metrics, and opens up the possibility of more
efficient computational methods.
Norman Ferns, Doina Precup. Bisimulation Metrics are Optimal Value Functions. The 30th Conference on Uncertainty in Artificial Intelligence, Ann Nicholson, Jul 2014, Quebec City, Canada. pp.10. ⟨hal-01101180⟩