101. Approximate Value Iteration for Risk-Aware Markov Decision Processes.
- Author
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Yu, Pengqian, Haskell, William B., and Xu, Huan
- Subjects
- *
APPROXIMATION algorithms , *MARKOV processes , *DYNAMIC programming , *VALUE at risk , *STOCHASTIC programming - Abstract
We consider large-scale Markov decision processes (MDPs) with a time-consistent risk measure of variability in cost under the risk-aware MDP paradigm. Previous studies showed that risk-aware MDPs, based on a minimax approach to handling risk, can be solved using dynamic programming for small- to medium-sized problems. However, due to the “curse of dimensionality,” MDPs that model real-life problems are typically prohibitively large for such approaches. In this technical note, we employ an approximate dynamic programming approach and develop a family of simulation-based algorithms to approximately solve large-scale risk-aware MDPs with time-consistent risk measures. In parallel, we develop a unified convergence analysis technique to derive sample complexity bounds for this new family of algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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