107 search results - page 3 / 22 » Approximate Linear Programming for Constrained Partially Obs... |
DATE
2008
IEEE
13 years 11 months ago
2008
IEEE
- The effectiveness of stochastic power management relies on the accurate system and workload model and effective policy optimization. Workload modeling is a machine learning proce...
ATAL
2010
Springer
13 years 6 months ago
2010
Springer
Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...
AIPS
2004
13 years 6 months ago
2004
Approximate linear programming (ALP) offers a promising framework for solving large factored Markov decision processes (MDPs) with both discrete and continuous states. Successful ...
ICML
2010
IEEE
13 years 6 months ago
2010
IEEE
Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions re...
NIPS
2003
13 years 6 months ago
2003
Approximate linear programming (ALP) has emerged recently as one of the most promising methods for solving complex factored MDPs with finite state spaces. In this work we show th...