.png){kind=icon}
.png){kind=icon}
Return
Simulation-based optimization of Markov reward processes
.png){kind=icon}
OA
AI
DOI：10.1109/9.905687.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a simulation-based algorithm for optimizing the average reward in a finite-state Markov reward process that depends on a set of parameters. As a special case, the method applies to Markov decision processes where optimization takes place within a parametrized set of policies. The algorithm relies on the regenerative structure of finite-state Markov processes, involves the simulation of a single sample path, and can be implemented online. A convergence result (with probability 1) is provided.
Keywords:
Markov reward processes
simulation-based optimization
stochastic approximation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
Organization
No organization information available