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    ICML
    2008
    IEEE
    133views Machine Learning» more  ICML 2008»
    An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning
    14 years 9 months ago
    An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning
    Download  www.cs.duke.edu
    We show that linear value-function approximation is equivalent to a form of linear model approximation. We then derive a relationship between the model-approximation error and the...
    Ronald Parr, Lihong Li, Gavin Taylor, Christopher ...
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    ICML
    1996
    IEEE
    162views Machine Learning» more  ICML 1996»
    Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning
    14 years 9 months ago
    Sensitive Discount Optimality: Unifying Discounted and Average Reward Reinforcement Learning
    Download  reference.kfupm.edu.sa
    Research in reinforcementlearning (RL)has thus far concentrated on two optimality criteria: the discounted framework, which has been very well-studied, and the averagereward frame...
    Sridhar Mahadevan
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    AAAI
    2008
    105views Intelligent Agents» more  AAAI 2008»
    Potential-based Shaping in Model-based Reinforcement Learning
    13 years 11 months ago
    Potential-based Shaping in Model-based Reinforcement Learning
    Download  www.aaai.org
    Potential-based shaping was designed as a way of introducing background knowledge into model-free reinforcement-learning algorithms. By identifying states that are likely to have ...
    John Asmuth, Michael L. Littman, Robert Zinkov
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    NIPS
    2007
    149views Information Technology» more  NIPS 2007»
    Online Linear Regression and Its Application to Model-Based Reinforcement Learning
    13 years 10 months ago
    Online Linear Regression and Its Application to Model-Based Reinforcement Learning
    Download  books.nips.cc
    We provide a provably efficient algorithm for learning Markov Decision Processes (MDPs) with continuous state and action spaces in the online setting. Specifically, we take a mo...
    Alexander L. Strehl, Michael L. Littman
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    ICML
    2010
    IEEE
    171views Machine Learning» more  ICML 2010»
    Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis
    13 years 10 months ago
    Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis
    Download  www.stat.lsa.umich.edu
    We introduce new, efficient algorithms for value iteration with multiple reward functions and continuous state. We also give an algorithm for finding the set of all nondominated a...
    Daniel J. Lizotte, Michael H. Bowling, Susan A. Mu...
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