This paper introduces a quantitative analysis framework for robust Markov Decision Processes (RMDPs) with $ω$-regular objectives. The research extends previous qualitative analyses by solving for the exact quantitative value, which represents the supremum of guaranteed satisfaction probability over all agent policies against adversarial environments. The authors demonstrate that both agent and environment admit pure, memoryless optimal policies and present a polynomial-time algorithm for quantitative parity on robust Markov chains, which is then used in a policy-iteration algorithm for RMDPs. Experimental results compare this new approach with reductions to stochastic games. AI
IMPACT Introduces a novel quantitative analysis method for robust decision-making under uncertainty, potentially improving AI agents' performance in complex environments.
RANK_REASON This is a research paper detailing a new analytical framework and algorithm for a specific type of Markov Decision Process. [lever_c_demoted from research: ic=1 ai=1.0]
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- $L_1$-maximal regularity for quasilinear second order differential equation with damped term
- Markov Decision Processes
- RMDPs
- Stochastic Games
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