Develops stochastic first-order methods for robust policy evaluation and approximate policy iteration in continuous-state robust MDPs, achieving 'O(1/ε^{2}) sample complexity for both evaluation and optimization.
Towards optimal offline reinforcement learning.arXiv preprint arXiv:2503.12283, 2025
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Robust Markov Decision Processes on Continuous State Spaces
Develops stochastic first-order methods for robust policy evaluation and approximate policy iteration in continuous-state robust MDPs, achieving 'O(1/ε^{2}) sample complexity for both evaluation and optimization.