Ahmadi, Mohamadreza and Ono, Masahiro and Ingham, Michel D. and Murray, Richard M. and Ames, Aaron D. (2020) Risk-Averse Planning Under Uncertainty. In: 2020 American Control Conference (ACC). IEEE , Piscataway, NJ, pp. 3305-3312. ISBN 9781538682661. https://resolver.caltech.edu/CaltechAUTHORS:20200109-092433424
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Abstract
We consider the problem of designing policies for partially observable Markov decision processes (POMDPs) with dynamic coherent risk objectives. Synthesizing risk-averse optimal policies for POMDPs requires infinite memory and thus undecidable. To overcome this difficulty, we propose a method based on bounded policy iteration for designing stochastic but finite state (memory) controllers, which takes advantage of standard convex optimization methods. Given a memory budget and optimality criterion, the proposed method modifies the stochastic finite state controller leading to sub-optimal solutions with lower coherent risk.
Item Type: | Book Section | |||||||||
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Additional Information: | © 2020 AACC. | |||||||||
DOI: | 10.23919/ACC45564.2020.9147792 | |||||||||
Record Number: | CaltechAUTHORS:20200109-092433424 | |||||||||
Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20200109-092433424 | |||||||||
Official Citation: | M. Ahmadi, M. Ono, M. D. Ingham, R. M. Murray and A. D. Ames, "Risk-Averse Planning Under Uncertainty," 2020 American Control Conference (ACC), Denver, CO, USA, 2020, pp. 3305-3312, doi: 10.23919/ACC45564.2020.9147792 | |||||||||
Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
ID Code: | 100583 | |||||||||
Collection: | CaltechAUTHORS | |||||||||
Deposited By: | Tony Diaz | |||||||||
Deposited On: | 09 Jan 2020 18:05 | |||||||||
Last Modified: | 16 Nov 2021 17:54 |
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