Risk-Averse Planning Under Uncertainty

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
DOI
Tag	ahm+20-acc
ID	2019l
Funding
Flags