46 search results - page 4 / 10 » A Sparse Sampling Algorithm for Near-Optimal Planning in Lar... |
CORR
2012
Springer
12 years 1 months ago
2012
Springer
Abstract— In this paper, we consider a class of continuoustime, continuous-space stochastic optimal control problems. Building upon recent advances in Markov chain approximation ...
AIPS
1998
13 years 7 months ago
1998
Planning methods for deterministic planning problems traditionally exploit factored representations to encode the dynamics of problems in terms of a set of parameters, e.g., the l...
PKDD
2010
Springer
13 years 3 months ago
2010
Springer
Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
SIGECOM
2010
ACM
13 years 10 months ago
2010
ACM
Motivated by the allocation problem facing publishers in display advertising we formulate the online assignment with forecast problem, a version of the online allocation problem w...
JAIR
2008
13 years 5 months ago
2008
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP i...