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This research aims to create a robust dynamic programming formulation to reduce sensitivity to uncertainty in transition probabilities.

Robust Dynamic Programming

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This research aims to create a robust dynamic programming formulation to reduce sensitivity to uncertainty in transition probabilities.
Developed a robust formulation for discrete time dynamic programming (DP) with ambiguous transition probabilities.
Proven results for finite and infinite horizon DP using a rectangularity property in sets of conditional measures.
Utilized techniques from previous studies to construct suitable sets of measures for optimal policy determination.
Proved that the robust formulation can extend known DP results to accommodate ambiguity.
Established that robust dynamic programming corresponds to stochastic zero-sum games with perfect information.

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Randomized formulation mitigates policy sensitivity to ambiguity in transition probabilities, suggesting effective strategies for optimal outcomes.

Garud Iyengar (2005) studied this question.

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Randomized formulation mitigates policy sensitivity to ambiguity in transition probabilities, suggesting effective strategies for optimal outcomes.