REVIEW 1 cited by
An Approximate Solution Method for Large Risk-Averse Markov Decision Processes
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Stochastic domains often involve risk-averse decision makers. While recent work has focused on how to model risk in Markov decision processes using risk measures, it has not addressed the problem of solving large risk-averse formulations. In this paper, we propose and analyze a new method for solving large risk-averse MDPs with hybrid continuous-discrete state spaces and continuous action spaces. The proposed method iteratively improves a bound on the value function using a linearity structure of the MDP. We demonstrate the utility and properties of the method on a portfolio optimization problem.
Forward citations
Cited by 1 Pith paper
-
A Bayesian Composite Risk Approach for Stochastic Optimal Control and Markov Decision Processes
The paper proposes Bayesian composite risk stochastic control and MDP models with belief-dependent policies, and proves dynamic programming and asymptotic convergence results.
Discussion (0). Continue with ORCID to comment.