A learning based method for obtaining feedback laws for nonlinear optimal control problems
is proposed. The learning problem is posed such that the open loop value function is its …

A learning based method for obtaining feedback laws for nonlinear optimal control problems
is proposed. The learning problem is posed such that the open loop value function is its …

A learning technique for finite horizon optimal control problems and its approximation based
on polynomials is analyzed. It allows to circumvent, in part, the curse dimensionality which is …

Q-learning has become an important part of the reinforcement learning toolkit since its
introduction in the dissertation of Chris Watkins in the 1980 s. In the original tabular …

We propose a function-learning methodology with a control-theoretical foundation. We
parametrise the approximating function as the solution to a control system on a reproducing …

In this paper, we propose a convergent Reinforcement Learning algorithm for solving
optimal control problems for which the state space and the time are continuous variables …

We consider the optimal control problem for a discrete time stochastic system yi+ 1= f (ξ, yi,
ui)+ σ (ξ, yi) wi+ 1 where the ui are controls, wi are the system disturbances, and ξ represents …

This paper presents a new formulation of data-driven, learning-based optimal control with
the Hamilton-Jacobi theory. In contrast to the common practice of rein-forcement learning for …

There has been a recent interest in imitation learning methods that are guaranteed to
produce a stabilizing control law with respect to a known system. Work in this area has …

The solutions of most nonlinear optimal control problems are given in the form of open-loop
optimal control which is computed from a given fixed initial condition. Optimal feedback …