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Summary: Reinforcement Learning for Mapping Instructions to Actions
S.R.K. Branavan, Harr Chen, Luke S. Zettlemoyer, Regina Barzilay
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
{branavan, harr, lsz, regina}@csail.mit.edu
Abstract
In this paper, we present a reinforce-
ment learning approach for mapping nat-
ural language instructions to sequences of
executable actions. We assume access to
a reward function that defines the qual-
ity of the executed actions. During train-
ing, the learner repeatedly constructs ac-
tion sequences for a set of documents, ex-
ecutes those actions, and observes the re-
sulting reward. We use a policy gradient
algorithm to estimate the parameters of a
log-linear model for action selection. We
apply our method to interpret instructions
in two domains -- Windows troubleshoot-
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