Jan 9, 2012 · This algorithm generates the splitting policies in a way that each pair of consecutive policies differs at exactly one state. The results are ...
as a convex combination of the occupancy measures of stationary policies, each selecting deterministic actions on the given.
This paper studies a discrete-time total-reward Markov decision process (MDP) with a given initial state distribution. A. (randomized) stationary policy can ...
An efficient algorithm is provided that presents the occupancy measure of a given policy as a convex combination of the occupancy measures of finitely many ...
Splitting Randomized Stationary Policies in Total-Reward Markov ...
www.bibsonomy.org › bibtex › dblp
Splitting Randomized Stationary Policies in Total-Reward Markov Decision Processes. E. Feinberg, and U. Rothblum. Math. Oper. Res., 37 (1): 129-153 (2012 ) ...
People also ask
What is the Markov decision process reward?
What is the optimal policy in the Markov decision process?
What is the equation for the Markov decision process?
What is the value function in MDP?
If this is possible for a given policy, we say that the policy can be split. In particular, we are interested in splitting a randomized stationary policy into ( ...
In this paper, we investigate a Markov decision process with constraints on a Borel state space with the expected total reward criterion.
Apr 28, 2015 · The initial state distribution is fixed. According to, for a given randomized stationary policy, its occupation measure as a convex combination ...
Rothblum "Splitting Randomized Stationary Policies in Total-Reward Markov Decision Processes" Mathematics of Operations Research , v.37 , 2012 , p.129. E.A. ...
This paper presents three conditions. Each of them guarantees the uniqueness of optimal policies of discounted Markov decision processes. The conditions ...