Learning Optimal Distributionally Robust Stochastic Control in Continuous State Spaces

Add to your list(s) Download to your calendar using vCal

• Jose Blanchet (Stanford University)
• Wednesday 12 November 2025, 14:00-14:40
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact nobody.

SCLW01 - Bridging Stochastic Control And Reinforcement Learning: Theories and Applications

We study data-driven learning of robust stochastic control for infinite-horizon systems with continuous state and action spaces. In many applied settings—supply chains, finance, manufacturing, services, and dynamic games—the state-transition mechanism is determined by system design, while available data capture the distributional properties of the stochastic inputs from the environment. For modeling and computational tractability, a decision maker often adopts a Markov control model with i.i.d. environment inputs, which can render learned policies fragile to internal dependence or external perturbations. We introduce a distributionally robust stochastic control paradigm that promotes policy reliability by introducing adaptive adversarial perturbations to the environment input, while preserving the modeling, statistical, and computational tractability of the Markovian formulation. From a modeling perspective, we examine two adversarial models—current-action-aware and current-action-unaware—leading to distinct dynamic behaviors and robust optimal policies. From a statistical learning perspective, we characterize optimal finite-sample minimax rates for uniform learning of the robust value function across a continuum of states under ambiguity sets defined by the $f_k$-divergence and Wasserstein distance. To efficiently compute the optimal robust policies, we further propose algorithms inspired by deep reinforcement learning methodologies. Finally, we demonstrate the applicability of the framework to real problems. (Joint work with Nian Si, Shengbo Wang, Zhengyuan Zhou)

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

• All CMS events
• Featured lists
• INI info aggregator
• Isaac Newton Institute Seminar Series
• School of Physical Sciences
• Seminar Room 1, Newton Institute
• bld31

Note that ex-directory lists are not shown.