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    ATAL
    2010
    Springer
    102views Intelligent Agents» more  ATAL 2010»
    Improving the performance of complex agent plans through reinforcement learning
    15 years 7 months ago
    Improving the performance of complex agent plans through reinforcement learning
    Download  www.dis.uniroma1.it
    Matteo Leonetti, Luca Iocchi
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    AAMAS
    2005
    Springer
    113views Intelligent Agents» more  AAMAS 2005»
    SMART (Stochastic Model Acquisition with ReinforcemenT) Learning Agents: A Preliminary Report
    15 years 5 months ago
    SMART (Stochastic Model Acquisition with ReinforcemenT) Learning Agents: A Preliminary Report
    Download  www.soi.city.ac.uk
    Christopher Child, Kostas Stathis
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    AAMAS
    2002
    Springer
    94views Intelligent Agents» more  AAMAS 2002»
    Using Landscape Theory to Measure Learning Difficulty for Adaptive Agents
    15 years 5 months ago
    Using Landscape Theory to Measure Learning Difficulty for Adaptive Agents
    Download  www.cs.usfca.edu
    Christopher H. Brooks, Edmund H. Durfee
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    ATAL
    2010
    Springer
    113views Intelligent Agents» more  ATAL 2010»
    Self-organisation in an agent network via learning
    15 years 1 months ago
    Self-organisation in an agent network via learning
    Download  www.aamas-conference.org
    Dayong Ye, Minjie Zhang, Danny Sutanto
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    IJCAI
    2001
    151views Artificial Intelligence» more  IJCAI 2001»
    R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
    15 years 7 months ago
    R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
    Download  jmlr.csail.mit.edu
    R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
    Ronen I. Brafman, Moshe Tennenholtz
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