Approximate dynamic programming with $(\min,+)$ linear function approximation for Markov decision processes

Markov Decision Processes (MDP) is an useful framework to cast optimal sequential decision making problems. Given any MDP the aim is to find the optimal action selection mechanism i.e., the optimal policy. Typically, the optimal policy ($u^*$) is obtained by substituting the optimal value-function ($J^*$) in the Bellman equation. Alternately $u^*$ is also obtained by learning the optimal state-action value function $Q^*$ known as the $Q$ value-function. However, it is difficult to compute the exact values of $J^*$ or $Q^*$ for MDPs with large number of states. Approximate Dynamic Programming (ADP) methods address this difficulty by computing lower dimensional approximations of $J^{$/$Q^$.} Most ADP methods employ linear function approximation (LFA), i.e., the approximate solution lies in a subspace spanned by a family of pre-selected basis functions. The approximation is obtain via a linear least squares projection of higher dimensional quantities and the $L_2$ norm plays an important role in convergence and error analysis. In this paper, we discuss ADP methods for MDPs based on LFAs in $(\min,+)$ algebra. Here the approximate solution is a $(\min,+)$ linear combination of a set of basis functions whose span constitutes a subsemimodule. Approximation is obtained via a projection operator onto the subsemimodule which is different from linear least squares projection used in ADP methods based on conventional LFAs. MDPs are not $(\min,+)$ linear systems, nevertheless, we show that the monotonicity property of the projection operator helps us to establish the convergence of our ADP schemes. We also discuss future directions in ADP methods for MDPs based on the $(\min,+)$ LFAs.