Semi-markov Decision Processes

The KTH Visit in Semi-Markov Processes

Existence of Optimal Policies for Semi-Markov Decision Processes Using Duality for Infinite Linear Programming

Semi-Markov decision processes on Borel spaces with deterministic kernels have many practical applications, particularly in inventory theory. Most of the results from general semi-Markov decision processes do not carry over to a deterministic kernel since such a kernel does not provide “smoothness.” We develop infinite dimensional linear programming theory for a general stochastic semi-Markov d...

Accelerated decomposition techniques for large discounted Markov decision processes

Many hierarchical techniques to solve large Markov decision processes (MDPs) are based on the partition of the state space into strongly connected components (SCCs) that can be classified into some levels. In each level, smaller problems named restricted MDPs are solved, and then these partial solutions are combined to obtain the global solution. In this paper, we first propose a novel algorith...

Regret optimality in semi-Markov decision processes with an absorbing set

The optimization problem of general utility case is considered for countable state semi-Markov decision processes. The regret-utility function is introduced as a function of two variables, one is a target value and the other is a present value. We consider the expectation of the regret-utility function incured until the reaching time to a given absorbing set. In order to characterize the regret...

Solving Generalized Semi-Markov Processes using Continuous Phase-Type Distributions

We introduce the generalized semi-Markov decision process (GSMDP) as an extension of continuous-time MDPs and semi-Markov decision processes (SMDPs) for modeling stochastic decision processes with asynchronous events and actions. Using phase-type distributions and uniformization, we show how an arbitrary GSMDP can be approximated by a discrete-time MDP, which can then be solved using existing M...