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Publication
151views
13 years 10 months ago
Robust Bayesian reinforcement learning through tight lower bounds
In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinfo...
Christos Dimitrakakis
ICML
2004
IEEE
16 years 20 days ago
Utile distinction hidden Markov models
This paper addresses the problem of constructing good action selection policies for agents acting in partially observable environments, a class of problems generally known as Part...
Daan Wierstra, Marco Wiering
SIGMETRICS
2000
ACM
105views Hardware» more  SIGMETRICS 2000»
15 years 4 months ago
Using the exact state space of a Markov model to compute approximate stationary measures
We present a new approximation algorithm based on an exact representation of the state space S, using decision diagrams, and of the transition rate matrix R, using Kronecker algeb...
Andrew S. Miner, Gianfranco Ciardo, Susanna Donate...
UAI
2000
15 years 1 months ago
PEGASUS: A policy search method for large MDPs and POMDPs
We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a mo...
Andrew Y. Ng, Michael I. Jordan
IJFCS
2008
130views more  IJFCS 2008»
14 years 12 months ago
Equivalence of Labeled Markov Chains
We consider the equivalence problem for labeled Markov chains (LMCs), where each state is labeled with an observation. Two LMCs are equivalent if every finite sequence of observat...
Laurent Doyen, Thomas A. Henzinger, Jean-Fran&cced...