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 …

For discrete-time nonlinear systems, this research is concerned with optimal control
problems (OCPs) with constrained cost, and a novel value iteration with constrained cost …

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 …

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

The applications of direct policy search in reinforcement learning and continuous control
have received increasing attention. In this work, we present novel theoretical results on the …

We examine online safe multi-agent reinforcement learning using constrained Markov
games in which agents compete by maximizing their expected total rewards under a …

The behaviour of a stochastic dynamical system may be largely influenced by those low-
probability, yet extreme events. To address such occurrences, this paper proposes an …

This paper addresses the infinite-horizon linear exponential quadratic Gaussian problem for
a class of stochastic systems with additive noise. A model-free generalized policy iteration …

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 …

We study the problem of computing deterministic optimal policies for constrained Markov
decision processes (MDPs) with continuous state and action spaces, which are widely …