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AAAI
2006
14 years 11 months ago
Incremental Least Squares Policy Iteration for POMDPs
We present a new algorithm, called incremental least squares policy iteration (ILSPI), for finding the infinite-horizon stationary policy for partially observable Markov decision ...
Hui Li, Xuejun Liao, Lawrence Carin
JAIR
2006
160views more  JAIR 2006»
14 years 9 months ago
Anytime Point-Based Approximations for Large POMDPs
The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact s...
Joelle Pineau, Geoffrey J. Gordon, Sebastian Thrun
ATAL
2010
Springer
14 years 11 months ago
Risk-sensitive planning in partially observable environments
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 ...
Janusz Marecki, Pradeep Varakantham
ATAL
2009
Springer
15 years 4 months ago
Online exploration in least-squares policy iteration
One of the key problems in reinforcement learning is balancing exploration and exploitation. Another is learning and acting in large or even continuous Markov decision processes (...
Lihong Li, Michael L. Littman, Christopher R. Mans...
TCOM
2011
114views more  TCOM 2011»
14 years 4 months ago
Iterative Receivers Based on Subblock Processing for Phase Noise Compensation in OFDM Systems
—An iterative algorithm employing decision feedback provided by either an equalizer or a channel decoder is proposed in order to compensate for the phase noise resulting from imp...
Myung-Kyu Lee, Kyeongcheol Yang, Kyungwhoon Cheun