Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings   Proceeding details   Article details         Authors         Search    
Bisimulation Metrics are Optimal Value Functions
Norman Ferns, Doina Precup
Abstract:
Bisimulation is a notion of behavioural equiva- lence on the states of a transition system. Its defi- nition has been extended to Markov decision pro- cesses, 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 opti- mal coupling of two copies of the original model. We prove the result in the general case of con- tinuous state spaces. This result has important implications in understanding the complexity of computing such metrics, and opens up the possi- bility of more efficient computational methods.
Keywords:
Pages: 210-219
PS Link:
PDF Link: /papers/14/p210-ferns.pdf
BibTex:
@INPROCEEDINGS{Ferns14,
AUTHOR = "Norman Ferns and Doina Precup",
TITLE = "Bisimulation Metrics are Optimal Value Functions",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "210--219"
}


hosted by DSL   •   site info   •   help