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ICML
2009
IEEE
16 years 7 months ago
Model-free reinforcement learning as mixture learning
We cast model-free reinforcement learning as the problem of maximizing the likelihood of a probabilistic mixture model via sampling, addressing both the infinite and finite horizo...
Nikos Vlassis, Marc Toussaint
EDM
2009
179views Data Mining» more  EDM 2009»
15 years 4 months ago
Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models
This paper describes a novel method to create a quantitative model of an educational content domain of related practice item-types using learning curves. By using a pairwise test t...
Philip I. Pavlik Jr., Hao Cen, Kenneth R. Koedinge...
ROBOCUP
2009
Springer
134views Robotics» more  ROBOCUP 2009»
16 years 23 days ago
Learning Complementary Multiagent Behaviors: A Case Study
As the reach of multiagent reinforcement learning extends to more and more complex tasks, it is likely that the diverse challenges posed by some of these tasks can only be address...
Shivaram Kalyanakrishnan, Peter Stone
GECCO
2007
Springer
187views Optimization» more  GECCO 2007»
16 years 12 days ago
Defining implicit objective functions for design problems
In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examp...
Sean Hanna
ACMICEC
2006
ACM
157views ECommerce» more  ACMICEC 2006»
16 years 6 days ago
Adaptive mechanism design: a metalearning approach
Auction mechanism design has traditionally been a largely analytic process, relying on assumptions such as fully rational bidders. In practice, however, bidders often exhibit unkn...
David Pardoe, Peter Stone, Maytal Saar-Tsechansky,...