Output details
11 - Computer Science and Informatics
University of Edinburgh
Chapter title
Expectation-Maximization Methods for Solving (PO)MDPs and Optimal Control Problems.
Type
C - Chapter in book
Publisher of book
Cambridge University Press
Book title
Bayesian Time Series Models
ISBN of book
9780521196765
Year of publication
2011
Number of additional authors
2
Additional information
<24> Originality: This was the first work to establish a general framework for using inference technology for solving general partially observable Markov Decision Processes with general horizons.
Significance: The paper establishes direct equivalence between control optimization and expectation maximization (EM) for inference, and thereby enabling the use of EM approaches for solving POMDPs. EM is now a popular approach for such problems as it is efficient due to the decoupling of forward and backward passes.
Rigour: The paper establishes direct mathematical equivalence between control optimization and expectation maximization for inference.
Interdisciplinary
-
Cross-referral requested
-
Research group
B - Institute for Adaptive & Neural Computation
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
