This paper steps back from the standard infinite horizon formulation of reinforcement learning problems to consider the simpler case of finite horizon problems. Although finite horizon problems may be solved using infinite horizon learning algorithms by recasting the problem as an infinite horizon problem over a state space extended to include time, we show that such an application of infinite ...

The management problem of water reservoirs can be formulated as a stochastic optimal control (SOC) problem, where the objective function is an aggregated cost that accounts for the interests acting in the water system (e.g. hydropower production, irrigation supply, etc.) and the design variable is the reservoirs release policy. Solving the SOC problem through stochastic dynamic programming is o...

We propose a new model for formalizing reward collection problems on graphs with dynamically generated rewards which may appear and disappear based on a stochastic model. The robot routing problem is modeled as a graph whose nodes are stochastic processes generating potential rewards over discrete time. The rewards are generated according to the stochastic process, but at each step, an existing...

This paper considers solving optimization problem for linear discrete time systems such that closed-loop discrete-time system is positive (i.e., all of its state variables have non-negative values) and also finite-time stable. For this purpose, by considering a quadratic cost function, an optimal controller is designed such that in addition to minimizing the cost function, the positivity proper...

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...

We consider the problem of designing a control law for a constrained linear system with bounded disturbances that ensures constraint satisfaction over an infinite horizon, while also guaranteeing that the closed-loop system has bounded gain. To this end, we propose a receding horizon control strategy based on the repeated calculation of optimal finite horizon feedback policies. We parameterize ...

This paper considers the problem of finding near-optimal Markovian randomized (MR) policies for finite-state-action, infinite-horizon, constrained risk-sensitive Markov decision processes (CRSMDPs). Constraints are in form standard expected discounted cost functions as well over finite and infinite horizons. We first show that aforementioned CRSMDP optimization possesses a solution if it is fea...