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@inproceedings{sadigh-cdc14,
author = {Dorsa Sadigh and Eric S. Kim and Samuel Coogan and Shankar Sastry and Sanjit A. Seshia},
title = {A Learning Based Approach to Control Synthesis of Markov Decision Processes for Linear Temporal Logic Specifications},
booktitle = {Proceedings of the 53rd IEEE Conference on Decision and Control (CDC)},
month = "December",
year = {2014},
pages = {1091--1096},
abstract = {We propose a method to synthesize a control
policy for a Markov decision process (MDP) such that the
resulting traces of the MDP satisfy a linear temporal logic
(LTL) property. We construct a product MDP that incorporates
a deterministic Rabin automaton generated from the desired
LTL property. The reward function of the product MDP is
defined from the acceptance condition of the Rabin automaton.
This construction allows us to apply techniques from learning
theory to the problem of synthesis for LTL specifications even
when the transition probabilities are not known a priori. We
prove that our method is guaranteed to find a controller that
satisfies the LTL property with probability one if such a policy
exists, and we suggest empirically with a case study in traffic
control that our method produces reasonable control strategies
even when the LTL property cannot be satisfied with probability
one.},
}