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    NIPS
    1994
    89views Information Technology» more  NIPS 1994»
    Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems
    13 years 6 months ago
    Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems
    Download  www.eecs.umich.edu
    Tommi Jaakkola, Satinder P. Singh, Michael I. Jord...
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    AAAI
    2006
    129views Intelligent Agents» more  AAAI 2006»
    On the Difficulty of Modular Reinforcement Learning for Real-World Partial Programming
    13 years 6 months ago
    On the Difficulty of Modular Reinforcement Learning for Real-World Partial Programming
    Download  www.cc.gatech.edu
    In recent years there has been a great deal of interest in "modular reinforcement learning" (MRL). Typically, problems are decomposed into concurrent subgoals, allowing ...
    Sooraj Bhat, Charles Lee Isbell Jr., Michael Matea...
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    AIPS
    2000
    102views Artificial Intelligence» more  AIPS 2000»
    Learning Planning Operators in Real-World, Partially Observable Environments
    13 years 6 months ago
    Learning Planning Operators in Real-World, Partially Observable Environments
    Download  www.aaai.org
    Matthew D. Schmill, Tim Oates, Paul R. Cohen
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    ICML
    1994
    IEEE
    151views Machine Learning» more  ICML 1994»
    Learning Without State-Estimation in Partially Observable Markovian Decision Processes
    13 years 8 months ago
    Learning Without State-Estimation in Partially Observable Markovian Decision Processes
    Download  www.eecs.umich.edu
    Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning control architectures for embedded agents. Unfortunately all of the theory and much ...
    Satinder P. Singh, Tommi Jaakkola, Michael I. Jord...
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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
    14 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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