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 …

Direct policy search has been widely applied in modern reinforcement learning and
continuous control. However, the theoretical properties of direct policy search on nonsmooth …

Model Predictive Path Integral (MPPI) is a recognized sampling-based approach for finite
horizon optimal control problems. However, the efficacy and computational efficiency of …

First-order policy optimization has been widely used in reinforcement learning. It guarantees
to find the optimal policy for the state-feedback linear quadratic regulator (LQR). However …

We introduce controlgym, a library of thirty-six industrial control settings, and ten infinite-
dimensional partial differential equation (PDE)-based control problems. Integrated within the …

This article introduces a novel approach called terminal sliding-mode control for achieving
time-synchronized convergence in multi-input–multi-output (MIMO) systems under …

This letter considers the optimization landscape of linear dynamic output feedback control
with robustness constraints. We consider the feasible set of all the stabilizing full-order …

This paper proposes model-based and model-free policy gradient methods (PGMs) for
designing dynamic output feedback controllers for discrete-time partially observable …

Reinforcement learning (RL) has great potential in sequential decision-making. At present,
the mainstream RL algorithms are data-driven, relying on millions of iterations and a large …

Direct policy search has achieved great empirical success in reinforcement learning. Many
recent studies have revisited its theoretical foundation for continuous control, which reveals …