This paper presents an axiomatic approach to finite Markov decision processes where the discount rate is zero. One of the principal difficulties in the no discounting case is that, even if attention is restricted to stationary policies, a strong overtaking optimal policy need not exists. We provide preference foundations for two criteria that do admit optimal policies: 0-discount optimality and average overtaking optimality. As a corollary of our results, we obtain conditions on a decision maker's preferences which ensure that an optimal policy exists. These results have implications for disciplines where dynamic programming problems arise, including automatic control, dynamic games, and economic development. Validerad;2023;Nivå 2;2023-02-2...
AbstractThe following optimality principle is established for finite undiscounted or discounted Mark...
AbstractThis paper studies the minimizing risk problems in Markov decision processes with countable ...
For semi-Markov decision processes with discounted rewards we derive the well known results regardin...
We consider a discrete time Markov Decision Process with infinite horizon. The criterion to be maxim...
AbstractThe following optimality principle is established for finite undiscounted or discounted Mark...
summary:In this paper there are considered Markov decision processes (MDPs) that have the discounted...
summary:In this paper there are considered Markov decision processes (MDPs) that have the discounted...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
This paper considers Markov decision processes (MDPs) with unbounded rates, as a function of state. ...
Abstract. We study the existence of optimal strategies and value func-tion of non stationary Markov ...
AbstractIn this paper, we study discounted Markov decision processes on an uncountable state space. ...
Optimality criteria for Markov decision processes have historically been based on a risk neutral for...
We study the existence of optimal strategies and value function of non stationary Markov decision pr...
We study the existence of optimal strategies and value function of non stationary Markov decision pr...
The Markov decision process is treated in a variety of forms or cases: finite or infinite horizon, w...
AbstractThe following optimality principle is established for finite undiscounted or discounted Mark...
AbstractThis paper studies the minimizing risk problems in Markov decision processes with countable ...
For semi-Markov decision processes with discounted rewards we derive the well known results regardin...
We consider a discrete time Markov Decision Process with infinite horizon. The criterion to be maxim...
AbstractThe following optimality principle is established for finite undiscounted or discounted Mark...
summary:In this paper there are considered Markov decision processes (MDPs) that have the discounted...
summary:In this paper there are considered Markov decision processes (MDPs) that have the discounted...
A Markov decision process (MDP) relies on the notions of state, describing the current situation of ...
This paper considers Markov decision processes (MDPs) with unbounded rates, as a function of state. ...
Abstract. We study the existence of optimal strategies and value func-tion of non stationary Markov ...
AbstractIn this paper, we study discounted Markov decision processes on an uncountable state space. ...
Optimality criteria for Markov decision processes have historically been based on a risk neutral for...
We study the existence of optimal strategies and value function of non stationary Markov decision pr...
We study the existence of optimal strategies and value function of non stationary Markov decision pr...
The Markov decision process is treated in a variety of forms or cases: finite or infinite horizon, w...
AbstractThe following optimality principle is established for finite undiscounted or discounted Mark...
AbstractThis paper studies the minimizing risk problems in Markov decision processes with countable ...
For semi-Markov decision processes with discounted rewards we derive the well known results regardin...