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    ICMLA
    2007
    92views Machine Learning» more  ICMLA 2007»
    Control of a re-entrant line manufacturing model with a reinforcement learning approach
    14 years 12 days ago
    Control of a re-entrant line manufacturing model with a reinforcement learning approach
    Download  www.smitlab.uc.edu
    This paper presents the application of a reinforcement learning (RL) approach for the near-optimal control of a re-entrant line manufacturing (RLM) model. The RL approach utilizes...
    José A. Ramírez-Hernández, Em...
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    AIPS
    2006
    141views Artificial Intelligence» more  AIPS 2006»
    Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling
    14 years 11 days ago
    Combining Stochastic Task Models with Reinforcement Learning for Dynamic Scheduling
    Download  www.aaai.org
    We view dynamic scheduling as a sequential decision problem. Firstly, we introduce a generalized planning operator, the stochastic task model (STM), which predicts the effects of ...
    Malcolm J. A. Strens
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    ATAL
    2010
    Springer
    181views Intelligent Agents» more  ATAL 2010»
    Basis function construction for hierarchical reinforcement learning
    14 years 2 days ago
    Basis function construction for hierarchical reinforcement learning
    Download  www.cs.brown.edu
    This paper introduces an approach to automatic basis function construction for Hierarchical Reinforcement Learning (HRL) tasks. We describe some considerations that arise when con...
    Sarah Osentoski, Sridhar Mahadevan
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    JAIR
    2002
    163views more  JAIR 2002»
    Efficient Reinforcement Learning Using Recursive Least-Squares Methods
    13 years 10 months ago
    Efficient Reinforcement Learning Using Recursive Least-Squares Methods
    Download  www.jair.org
    The recursive least-squares (RLS) algorithm is one of the most well-known algorithms used in adaptive filtering, system identification and adaptive control. Its popularity is main...
    Xin Xu, Hangen He, Dewen Hu
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    NECO
    2007
    150views more  NECO 2007»
    Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule
    13 years 10 months ago
    Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule
    Download  eprints.pascal-network.org
    Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is ...
    Dorit Baras, Ron Meir
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