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Risk-Averse Planning Under Uncertainty

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
Related URLs:
URLURL TypeDescription
https://doi.org/10.23919/ACC45564.2020.9147792DOIArticle
https://arxiv.org/abs/1909.12499arXivDiscussion Paper
ORCID:
AuthorORCID
Ahmadi, Mohamadreza0000-0003-1447-3012
Murray, Richard M.0000-0002-5785-7481
Ames, Aaron D.0000-0003-0848-3177
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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