Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings         Authors   Author's Info   Article details         Search    
Dynamic Programming for Structured Continuous Markov Decision Problems
Zhengzhu Feng, Richard Dearden, Nicolas Meuleau, Richard Washington
Abstract:
We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamically partitioned into regions where the value function is the same throughout the region. We first describe the algorithm for piecewise constant representations. We then extend it to piecewise linear representations, using techniques from POMDPs to represent and reason about linear surfaces efficiently. We show that for complex, structured problems, our approach exploits the natural structure so that optimal solutions can be computed efficiently.
Keywords: null
Pages: 154-161
PS Link:
PDF Link: /papers/04/p154-feng.pdf
BibTex:
@INPROCEEDINGS{Feng04,
AUTHOR = "Zhengzhu Feng and Richard Dearden and Nicolas Meuleau and Richard Washington",
TITLE = "Dynamic Programming for Structured Continuous Markov Decision Problems",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "154--161"
}


hosted by DSL   •   site info   •   help