Gradient-based methods have been widely used for system design and optimization in
diverse application domains. Recently, there has been a renewed interest in studying …

Transient growth and resolvent analyses are routinely used to assess nonasymptotic
properties of fluid flows. In particular, resolvent analysis can be interpreted as a special case …

We study sequential decision-making problems in which each agent aims to maximize the
expected total reward while satisfying a constraint on the expected total utility. We employ …

Natural policy gradient (NPG) methods are among the most widely used policy optimization
algorithms in contemporary reinforcement learning. This class of methods is often applied in …

Prompt-tuning is an emerging strategy to adapt large language models (LLM) to
downstream tasks by learning a (soft-) prompt parameter from data. Despite its success in …

Policy optimization (PO) is a key ingredient for modern reinforcement learning (RL). For
control design, certain constraints are usually enforced on the policies to optimize …

The linear quadratic regulator is the fundamental problem of optimal control. Its state
feedback version was set and solved in the early 1960s. However, the static output feedback …

This article considers a distributed reinforcement learning problem for decentralized linear
quadratic (LQ) control with partial state observations and local costs. We propose a zero …

This paper studies a class of partially observed Linear Quadratic Gaussian (LQG) problems
with unknown dynamics. We establish an end-to-end sample complexity bound on learning …

The softmax policy gradient (PG) method, which performs gradient ascent under softmax
policy parameterization, is arguably one of the de facto implementations of policy …