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» Finding Structure in Reinforcement Learning
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120
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ATAL
2004
Springer
15 years 9 months ago
Best-Response Multiagent Learning in Non-Stationary Environments
This paper investigates a relatively new direction in Multiagent Reinforcement Learning. Most multiagent learning techniques focus on Nash equilibria as elements of both the learn...
Michael Weinberg, Jeffrey S. Rosenschein
ITNG
2007
IEEE
15 years 9 months ago
Input Fuzzy Modeling for the Recognition of Handwritten Hindi Numerals
This paper presents the recognition of Handwritten Hindi Numerals based on the modified exponential membership function fitted to the fuzzy sets derived from normalized distance f...
Madasu Hanmandlu, J. Grover, Vamsi Krishna Madasu,...
124
Voted
ICMLA
2010
15 years 1 months ago
Robust Learning for Adaptive Programs by Leveraging Program Structure
Abstract--We study how to effectively integrate reinforcement learning (RL) and programming languages via adaptation-based programming, where programs can include non-deterministic...
Jervis Pinto, Alan Fern, Tim Bauer, Martin Erwig
AIED
2009
Springer
15 years 10 months ago
To Elicit Or To Tell: Does It Matter?
Abstract. While high interactivity has been one of the main characteristics of oneon-one human tutoring, a great deal of controversy surrounds the issue of whether interactivity is...
Min Chi, Pamela W. Jordan, Kurt VanLehn, Diane J. ...
136
Voted
ATAL
2009
Springer
15 years 1 months ago
Replicator Dynamics for Multi-agent Learning: An Orthogonal Approach
Today's society is largely connected and many real life applications lend themselves to be modeled as multi-agent systems. Although such systems as well as their models are d...
Michael Kaisers, Karl Tuyls