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ATAL
2004
Springer
14 years 2 months ago
Communication for Improving Policy Computation in Distributed POMDPs
Distributed Partially Observable Markov Decision Problems (POMDPs) are emerging as a popular approach for modeling multiagent teamwork where a group of agents work together to joi...
Ranjit Nair, Milind Tambe, Maayan Roth, Makoto Yok...
NN
2010
Springer
125views Neural Networks» more  NN 2010»
13 years 7 months 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...
GECCO
2008
Springer
179views Optimization» more  GECCO 2008»
13 years 9 months ago
Emergent architecture in self organized swarm systems for military applications
Many sectors of the military are interested in Self-Organized (SO) systems because of their flexibility, versatility and economics. The military is researching and employing auto...
Dustin J. Nowak, Gary B. Lamont, Gilbert L. Peters...
AAAI
2007
13 years 11 months ago
Optimizing Anthrax Outbreak Detection Using Reinforcement Learning
The potentially catastrophic impact of a bioterrorist attack makes developing effective detection methods essential for public health. In the case of anthrax attack, a delay of ho...
Masoumeh T. Izadi, David L. Buckeridge
IJCAI
2001
13 years 10 months ago
Complexity of Probabilistic Planning under Average Rewards
A general and expressive model of sequential decision making under uncertainty is provided by the Markov decision processes (MDPs) framework. Complex applications with very large ...
Jussi Rintanen