View source for Risk-Averse Planning Under Uncertainty

{{Paper
|Title=Risk-Averse Planning Under Uncertainty
|Authors=Mohamadreza Ahmadi, Masahiro Ono, Michel D. Ingham, Richard M. Murray, 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.
|URL=https://arxiv.org/abs/1909.12499
|Type=Conference paper
|ID=2019l
|Tag=ahm+20-acc
}}