Risk-Averse Planning Under Uncertainty

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Title Risk-Averse Planning Under Uncertainty
Authors Mohamadreza Ahmadi, Masahiro Ono, Michel D. Ingham, Richard M. Murray and Aaron D. Ames
Source 2020 American Control Conference (ACC)
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.
Type Conference paper
URL https://arxiv.org/abs/1909.12499
Tag ahm+20-acc
ID 2019l