Continuity of Generalized Semi-Markov Processes

The KTH Visit in Semi-Markov Processes

Overshoot Functionals for Almost Semi-continuous Processes Defined on a Markov Chain

The distribution of extrema and overjump functionals for the semi-continuous processes (processes that intersect positive or negative level continuously) on a Markov chain were considered by many authors( for instance, see [1] [3]). In the paper [4] the distribution of extrema for almost semi-continuous processes were treated (the processes that intersect positive or negative level by exponenti...

Utilizing Generalized Learning Automata for Finding Optimal Policies in MMDPs

Multi agent Markov decision processes (MMDPs), as the generalization of Markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for Multi agent Reinforcement Learning. In this paper, a generalized learning automata based algorithm for finding optimal policies in MMDP is proposed. In the proposed algorithm, MMDP ...

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...

Solving Generalized Semi-Markov Decision 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...