This article presents a reinforcement learning framework for continuous-time dynamical
systems without a priori discretization of time, state, and action. Basedonthe Hamilton-Jacobi …

One of the basic problems of machine learning is deciding how to act in an uncertain world.
For example, if I want my robot to bring me a cup of coffee, it must be able to compute the …

The choice of the control frequency of a system has a relevant impact on the ability of
reinforcement learning algorithms to learn a highly performing policy. In this paper, we …

In this paper, we propose Q-learning algorithms for continuous-time deterministic optimal
control problems with Lipschitz continuous controls. A new class of Hamilton-Jacobi …

We propose a method for designing policies for convex stochastic control problems
characterized by random linear dynamics and convex stage cost. We consider policies that …

The policy gradient method is a promising approach in reinforcement learning (RL) for
optimizing action policy parameters in order to maximize average reward. The natural …

The concept of the value-gradient is introduced and developed in the context of
reinforcement learning. It is shown that by learning the value-gradients exploration or …

In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to
address complex control tasks. In a Markov Decision Process (MDP), the framework typically …

This paper deals with solving continuous time, state and action optimization problems in
stochastic settings, using reinforcement learning algorithms, and considers the policy …

This thesis proposes and studies a methodology for designing controllers for nonlinear
dynamic systems. We are interested in state feedback controllers (policies) that stabilize the …