We consider a receding horizon approach as an approximate solution to two-person zero-sum Markov games with infinite horizon discounted cost and average cost criteria. We first present error bounds from the optimal equilibrium value of the game when both players take correlated equilibrium receding horizon policies that are based on exact or approximate solutions of receding finite horizon subg...

This work investigates the use of finite abstractions to study the finite-horizon probabilistic invariance problem over Stochastic MaxPlus-Linear (SMPL) systems. SMPL systems are probabilistic extensions of discrete-event MPL systems that are widely employed in the engineering practice for timing and synchronisation studies. We construct finite abstractions by re-formulating the SMPL system as ...

The normalized maximum likelihood distribution achieves minimax coding (log-loss) regret given a fixed sample size, or horizon, n. It generally requires that n be known in advance. Furthermore, extracting the sequential predictions from the normalized maximum likelihood distribution is computationally infeasible for most statistical models. Several computationally feasible alternative strategie...

A receding horizon Kalman finite-impulse response (FIR) filter is suggested for continuous-time systems, combining the Kalman filter with the receding horizon strategy. In the suggested filter, the horizon initial state is assumed to be unknown. It can always be obtained irrespective of unknown information on the horizon initial state. The filter may be the first stochastic FIR form for continu...

The aim of this paper is to provide improvments to Pontryagin principles in infinite-horizon discrete-time framework when the space of states and of space of controls are infinite-dimensional. We use the method of reduction to finite horizon and several functional-analytic lemmas to realize our aim.

We consider a controlled state equation of parabolic type on the halfline (0,+∞) with boundary conditions of Dirichlet type in which the unknown is equal to the sum of the control and of a white noise in time. We study finite horizon and infinite horizon optimal control problem related by menas of backward stochastic differential equations.

We consider an optimal control problem with a deterministic finite horizon and state variable dynamics given by a Markovswitching jump-diffusion stochastic differential equation. Our main results extend the dynamic programming technique to this larger family of stochastic optimal control problems. More specifically, we provide a detailed proof of Bellman’s optimality principle (or dynamic progr...

Experience with infinite-horizon state-space model predictive control confirms that the algorithm offers several advantages over the more conventional finite-horizon step-response based model predictive control algorithms, particularly in the specification of sample time and handling a wide range of process time constants. Examples illustrate our use of state space based model predictive contro...

This paper addresses constrained Markov decision processes, with expected discounted total cost criteria, which are controlled by nonrandomized policies. A dynamic programming approach is used to construct optimal policies. The convergence of the series of finite horizon value functions to the infinite horizon value function is also shown. A simple example illustrating an application is presented.