Constrained risk-averse Markov decision processes

Title	Constrained risk-averse Markov decision processes
Authors	Mohamadreza Ahmadi, Ugo Rosolia, Michel D Ingham, Richard M Murray and Aaron D Ames
Source	35th AAAI Conference on Artificial Intelligence (AAAI-21)
Abstract	We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints. We begin by formulating the problem in a Lagrangian framework. Under the assumption that the risk objectives and constraints can be represented by a Markov risk transition mapping, we propose an optimization-based method to synthesize Markovian policies that lower-bound the constrained risk-averse problem. We demonstrate that the formulated optimization problems are in the form of difference convex programs (DCPs) and can be solved by the disciplined convex-concave programming (DCCP) framework. We show that these results generalize linear programs for constrained MDPs with total discounted expected costs and constraints. Finally, we illustrate the effectiveness of the proposed method with numerical experiments on a rover navigation problem involving conditional-value-at-risk (CVaR) and entropic-value-at-risk (EVaR) coherent risk measures.
Type	Conference paper
URL	http://www.cds.caltech.edu/~murray/preprints/aaaYY-place.pdf
DOI
Tag	MA+21-aaai
ID	2021e
Funding
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