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NIPS
1994
15 years 5 months ago
Reinforcement Learning with Soft State Aggregation
It is widely accepted that the use of more compact representations than lookup tables is crucial to scaling reinforcement learning (RL) algorithms to real-world problems. Unfortun...
Satinder P. Singh, Tommi Jaakkola, Michael I. Jord...
CORR
2008
Springer
189views Education» more  CORR 2008»
15 years 4 months ago
Algorithms for Dynamic Spectrum Access with Learning for Cognitive Radio
We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperati...
Jayakrishnan Unnikrishnan, Venugopal V. Veeravalli
ETS
2006
IEEE
114views Hardware» more  ETS 2006»
15 years 4 months ago
From Research Resources to Learning Objects: Process Model and Virtualization Experiences
Typically, most research and academic institutions own and archive a great amount of objects and research related resources that have been produced, used and maintained over long ...
José Luis Sierra, Alfredo Fernández-...
JMLR
2006
153views more  JMLR 2006»
15 years 4 months ago
Collaborative Multiagent Reinforcement Learning by Payoff Propagation
In this article we describe a set of scalable techniques for learning the behavior of a group of agents in a collaborative multiagent setting. As a basis we use the framework of c...
Jelle R. Kok, Nikos A. Vlassis
PAMI
2008
302views more  PAMI 2008»
15 years 4 months ago
Learning to Detect Moving Shadows in Dynamic Environments
We propose a novel adaptive technique for detecting moving shadows and distinguishing them from moving objects in video sequences. Most methods for detecting shadows work in a stat...
Ajay J. Joshi, Nikolaos Papanikolopoulos