Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings         Authors   Author's Info   Article details         Search    
New inference strategies for solving Markov Decision Processes using reversible jump MCMC
Matthias Hoffman, Hendrik Kueck, Nando de Freitas, Arnaud Doucet
Abstract:
In this paper we build on previous work which uses inferences techniques, in particular Markov Chain Monte Carlo (MCMC) methods, to solve parameterized control problems. We propose a number of modifications in order to make this approach more practical in general, higher-dimensional spaces. We first introduce a new target distribution which is able to incorporate more reward information from sampled trajectories. We also show how to break strong correlations between the policy parameters and sampled trajectories in order to sample more freely. Finally, we show how to incorporate these techniques in a principled manner to obtain estimates of the optimal policy.
Keywords: null
Pages: 223-231
PS Link:
PDF Link: /papers/09/p223-hoffman.pdf
BibTex:
@INPROCEEDINGS{Hoffman09,
AUTHOR = "Matthias Hoffman and Hendrik Kueck and Nando de Freitas and Arnaud Doucet",
TITLE = "New inference strategies for solving Markov Decision Processes using reversible jump MCMC",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "223--231"
}


hosted by DSL   •   site info   •   help