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• W2576376475 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." @default.
• W2576376475 created "2017-01-26" @default.
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• W2576376475 date "2018-09-01" @default.
• W2576376475 modified "2023-10-17" @default.
• W2576376475 title "Approximate Value Iteration for Risk-Aware Markov Decision Processes" @default.
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• W2576376475 doi "https://doi.org/10.1109/tac.2018.2790261" @default.
• W2576376475 hasPublicationYear "2018" @default.
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