In many areas of applied mathematics, decentralization of information is a ubiquitous
attribute affecting how to approach a stochastic optimization, decision and estimation, or …

In this paper, we introduce a regularized mean-field game and study learning of this game
under an infinite-horizon discounted reward function. Regularization is introduced by adding …

The average cost optimality is known to be a challenging problem for partially observable
stochastic control, with few results available beyond the finite state, action, and …

We study finite-time horizon continuous-time linear-convex reinforcement learning problems
in an episodic setting. In this problem, the unknown linear jump-diffusion process is …

This paper proves continuity of value functions in discounted periodic‐review single‐
commodity total‐cost inventory control problems with continuous inventory levels, fixed …

Weak Feller property of controlled and control-free Markov chains leads to many desirable
properties. In control-free setups this leads to the existence of invariant probability measures …

We consider finite-and infinite-horizon Markov decision processes (MDPs) with unknown
state-transition probabilities. They are assumed to belong to certain ambiguity sets, and the …

This paper proposes a relaxed control regularization with general exploration rewards to
design robust feedback controls for multidimensional continuous-time stochastic exit time …

A common approach to solve partially observable Markov decision processes (POMDPs) is
transforming them into Makov decision processes (MDPs) defined on information states …

Feedback control actively dissipates uncertainty from a dynamical system by means of
actuation. We develop a notion of “control capacity” that gives a fundamental limit (in bits) on …