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ICRA
2010
IEEE
133views Robotics» more  ICRA 2010»
15 years 4 days ago
Variable resolution decomposition for robotic navigation under a POMDP framework
— Partially Observable Markov Decision Processes (POMDPs) offer a powerful mathematical framework for making optimal action choices in noisy and/or uncertain environments, in par...
Robert Kaplow, Amin Atrash, Joelle Pineau
IJRR
2010
162views more  IJRR 2010»
15 years 3 days ago
Planning under Uncertainty for Robotic Tasks with Mixed Observability
Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been app...
Sylvie C. W. Ong, Shao Wei Png, David Hsu, Wee Sun...
NN
2010
Springer
125views Neural Networks» more  NN 2010»
15 years 1 days ago
Parameter-exploring policy gradients
We present a model-free reinforcement learning method for partially observable Markov decision problems. Our method estimates a likelihood gradient by sampling directly in paramet...
Frank Sehnke, Christian Osendorfer, Thomas Rü...
GLOBECOM
2010
IEEE
14 years 11 months ago
Maximize Secondary User Throughput via Optimal Sensing in Multi-Channel Cognitive Radio Networks
In a cognitive radio network, the full-spectrum is usually divided into multiple channels. However, due to the hardware and energy constraints, a cognitive user (also called second...
Shimin Gong, Ping Wang, Wei Liu, Wei Yuan
ICTAI
2010
IEEE
14 years 11 months ago
A Closer Look at MOMDPs
Abstract--The difficulties encountered in sequential decisionmaking problems under uncertainty are often linked to the large size of the state space. Exploiting the structure of th...
Mauricio Araya-López, Vincent Thomas, Olivi...