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    IJCNN
    2008
    IEEE
    110views Neural Networks» more  IJCNN 2008»
    Self-organizing neural models integrating rules and reinforcement learning
    14 years 2 months ago
    Self-organizing neural models integrating rules and reinforcement learning
    Download  www3.ntu.edu.sg
    — Traditional approaches to integrating knowledge into neural network are concerned mainly about supervised learning. This paper presents how a family of self-organizing neural m...
    Teck-Hou Teng, Zhong-Ming Tan, Ah-Hwee Tan
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    WEBI
    2009
    Springer
    120views Internet Technology» more  WEBI 2009»
    Adapting Reinforcement Learning for Trust: Effective Modeling in Dynamic Environments
    14 years 2 months ago
    Adapting Reinforcement Learning for Trust: Effective Modeling in Dynamic Environments
    Download  mas.cmpe.boun.edu.tr
    —In open multiagent systems, agents need to model their environments in order to identify trustworthy agents. Models of the environment should be accurate so that decisions about...
    Özgür Kafali, Pinar Yolum
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    EWRL
    2008
    186views Machine Learning» more  EWRL 2008»
    Efficient Reinforcement Learning in Parameterized Models: Discrete Parameter Case
    13 years 9 months ago
    Efficient Reinforcement Learning in Parameterized Models: Discrete Parameter Case
    Download  webee.technion.ac.il
    We consider reinforcement learning in the parameterized setup, where the model is known to belong to a parameterized family of Markov Decision Processes (MDPs). We further impose ...
    Kirill Dyagilev, Shie Mannor, Nahum Shimkin
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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
    13 years 9 months 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
    13 years 9 months 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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