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» MDPs: Learning in Varying Environments
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GECCO
2006
Springer
159views Optimization» more  GECCO 2006»
14 years 1 months ago
Multi-step environment learning classifier systems applied to hyper-heuristics
Heuristic Algorithms (HA) are very widely used to tackle practical problems in operations research. They are simple, easy to understand and inspire confidence. Many of these HAs a...
Javier G. Marín-Blázquez, Sonia Schu...
NIPS
2001
13 years 11 months ago
The Steering Approach for Multi-Criteria Reinforcement Learning
We consider the problem of learning to attain multiple goals in a dynamic environment, which is initially unknown. In addition, the environment may contain arbitrarily varying ele...
Shie Mannor, Nahum Shimkin
ICASSP
2011
IEEE
13 years 1 months ago
Dynamic selection of a speech enhancement method for robust speech recognition in moving motorcycle environment
We present a speech pre-processing scheme (SPPS) for robust speech recognition in the moving motorcycle environment. The SPPS is dynamically adapted during the run-time operation ...
Iosif Mporas, Todor Ganchev, Otilia Kocsis, Nikos ...
AI
2002
Springer
13 years 9 months ago
Multiagent learning using a variable learning rate
Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on ...
Michael H. Bowling, Manuela M. Veloso
DAGM
2010
Springer
13 years 10 months ago
Learning of Optimal Illumination for Material Classification
We present a method to classify materials in illumination series data. An illumination series is acquired using a device which is capable to generate arbitrary lighting environment...
Markus Jehle, Christoph Sommer, Bernd Jähne