We consider a government that aims at reducing the debt-to-gross domestic product (GDP) ratio of a country. The government observes the level of the debt-to-GDP ratio and an indicator of the state of the economy, but does not directly observe the development of the underlying macroeconomic...

Computing the solution to a stochastic optimal control problem is difficult. A method of approximating a solution to a given stochatic optimal problem was developed in [1]. This paper describes a suite of Matlab functions implementing this method of approximating a solution to a given continuous...

Strategies for constructing a Markov decision chain approximating a continuous-time finite-horizon optimal control problem are investigated. Some simple, analytically soluble, examples are treated and low computational complexity is reported. Extensions to the method and implementation are...

We analyze a W-configuration assemble-to-order system with random lead times, random arrival of demand, and lost sales, in continuous time. Specifically, we assume exponentially distributed production and demand inter-arrival times. We formulate the problem as an infinite-horizon Markov decision...

We consider two control problems on a finite horizon; one stochastic and the other deterministic. In both problems the running cost and the terminal cost are the same. The controllable input in both problems is of an additive nature with cost proportional to the input (which can be both positive...

Neural Control (NC) is a growing field in stochastic optimal control applied to various dynamical systems such as quadcopter. NC is a non-parametric, learning based computational scheme, capable of handling high dimensional control problems. In this paper, “NC” principles have been applied...