Fully observed Markov decision processes

Abstract

A Markov decision process (MDP) is a Markov process with feedback control. That is, as illustrated in Figure 6.1, a decision-maker (controller) uses the state x k of the Markov process at each time k to choose an action u k . This action is fed back to the Markov process and controls the transition matrix P ( u k ). This in turn determines the probability that the Markov process jumps to a particular state x k +1 at time k + 1 and so on. The aim of the decision-maker is to choose a sequence of actions over a time horizon to minimize a cumulative cost function associated with the expected value of the trajectory of the Markov process. MDPs arise in stochastic optimization models in telecommunication networks, discrete event systems, inventory control, finance, investment and health planning. Also POMDPs can be viewed as continuous state MDPs. This chapter gives a brief description of MDPs which provides a starting point for POMDPs. The main result is that optimal choice of actions by the controller in Figure 6.1 is obtained by solving a backward stochastic dynamic programming problem. Finite state finite horizon MDP Let k = 0, 1, …, N denote discrete time. N is called the time horizon or planning horizon. In this section we consider MDPs where the horizon N is finite. The finite state MDP model consists of the following ingredients: 1. X = {1, 2, …, X } denotes the state space and x k ∈ X denotes the state of the controlled Markov chain at time k = 0, 1, …, N . 2. U = {1, 2, …, U } denotes the action space. The elements u ∈ U are called actions. In particular, u k ∈ U denotes the action chosen at time k . 3. For each action u ∈ U and time k ∈ {0, …, N −1}, P ( u , k ) denotes an X × X transition probability matrix with elements P ij ( u , k ) = ℙ( x k +1 = j | x k = i , u k = u ), i , j ∈ X . 4. For each state i ∈ X , action u ∈ U and time k ∈ {0, 1, …, N −1}, the scalar c ( i , u , k ) denotes the one-stage cost incurred by the decision-maker (controller).