Learning algorithms have become an integral component to modern engineering solutions.
Examples range from self-driving cars and recommender systems to finance and even …

We introduce a framework for eliminating the computation of controller gain functions in
partial differential equation (PDE) control. We learn the nonlinear operator from the plant …

Unlike ODEs, whose models involve system matrices and whose controllers involve vector
or matrix gains, PDE models involve functions in those roles—functional coefficients …

Direct data-driven design methods for the linear quadratic regulator (LQR) mainly use offline
or episodic data batches, and their online adaptation has been acknowledged as an open …

The convergence of policy gradient algorithms hinges on the optimization landscape of the
underlying optimal control problem. Theoretical insights into these algorithms can often be …

While the techniques in optimal control theory are often model-based, the policy optimization
(PO) approach directly optimizes the performance metric of interest. Even though it has been …

A key challenge in lifelong reinforcement learning (RL) is the loss of plasticity, where
previous learning progress hinders an agent's adaptation to new tasks. While regularization …

We study a model-free federated linear quadratic regulator (LQR) problem where M agents
with unknown, distinct yet similar dynamics collaboratively learn an optimal policy to …

We revisit in this letter the discrete-time linear quadratic regulator (LQR) problem from the
perspective of receding-horizon policy gradient (RHPG), a newly developed model-free …

In this paper, we propose a robust reinforcement learning method for a class of linear
discrete-time systems to handle model mismatches that may be induced by sim-to-real gap …