Name
Title | ||
|---|---|---|
A Convex Optimization Approach to Distributionally Robust Markov Decision Processes With Wasserstein Distance. |
Abstract | ||
|---|---|---|
We consider the problem of constructing control policies that are robust against distribution errors in the model parameters of Markov decision processes. The Wasserstein metric is used to model the ambiguity set of admissible distributions. We prove the existence and optimality of Markov policies and develop convex optimization-based tools to compute and analyze the policies. Our methods, which a... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LCSYS.2017.2711553 | IEEE Control Systems Letters |
Keywords | Field | DocType |
Robustness,Markov processes,Tools,Measurement,Mathematical model,Optimization,Games | Mathematical optimization,Partially observable Markov decision process,Markov model,Markov chain,Markov decision process,Duality (optimization),Wasserstein metric,Markov kernel,Convex optimization,Mathematics | Journal |
Volume | Issue | ISSN |
1 | 1 | 2475-1456 |
Citations | PageRank | References |
4 | 0.42 | 15 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Insoon Yang | 1 | 35 | 9.17 |