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    EDM
    2009
    110views Data Mining» more  EDM 2009»
    Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions o
    15 years 2 months ago
    Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions o
    Download  www.educationaldatamining.org
    A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different in...
    Mingyu Feng, Joseph Beck, Neil T. Heffernan
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    ESANN
    2008
    278views Neural Networks» more  ESANN 2008»
    Learning to play Tetris applying reinforcement learning methods
    15 years 6 months ago
    Learning to play Tetris applying reinforcement learning methods
    Download  www.dice.ucl.ac.be
    In this paper the application of reinforcement learning to Tetris is investigated, particulary the idea of temporal difference learning is applied to estimate the state value funct...
    Alexander Groß, Jan Friedland, Friedhelm Sch...
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    ICML
    2009
    IEEE
    160views Machine Learning» more  ICML 2009»
    The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning
    16 years 5 months ago
    The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning
    Download  www.research.rutgers.edu
    The purpose of this paper is three-fold. First, we formalize and study a problem of learning probabilistic concepts in the recently proposed KWIK framework. We give details of an ...
    Carlos Diuk, Lihong Li, Bethany R. Leffler
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    ICML
    2009
    IEEE
    172views Machine Learning» more  ICML 2009»
    Model-free reinforcement learning as mixture learning
    16 years 5 months ago
    Model-free reinforcement learning as mixture learning
    Download  user.cs.tu-berlin.de
    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
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    ICML
    2008
    IEEE
    135views Machine Learning» more  ICML 2008»
    Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs
    16 years 5 months ago
    Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs
    Download  mapleleaf.csail.mit.edu
    Partially Observable Markov Decision Processes (POMDPs) have succeeded in planning domains that require balancing actions that increase an agent's knowledge and actions that ...
    Finale Doshi, Joelle Pineau, Nicholas Roy
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