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arXiv · 1909.12499

Risk-Averse Planning Under Uncertainty

Mohamadreza Ahmadi·Masahiro Ono·Michel D. Ingham·Richard M. Murray·Aaron D. Ames
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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.

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Mohamadreza Ahmadi, Masahiro Ono, Michel D. Ingham, Richard M. Murray, Aaron D. Ames. 2019-09-27. Risk-Averse Planning Under Uncertainty. https://arxiv.org/abs/1909.12499

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