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    ATAL
    2009
    Springer
    77views Intelligent Agents» more  ATAL 2009»
    Learning with whom to communicate using relational reinforcement learning
    14 years 3 months ago
    Learning with whom to communicate using relational reinforcement learning
    Download  www.aamas-conference.org
    Marc J. V. Ponsen, Tom Croonenborghs, Karl Tuyls, ...
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    FLAIRS
    2003
    117views Artificial Intelligence» more  FLAIRS 2003»
    Subgoal Discovery for Hierarchical Reinforcement Learning Using Learned Policies
    13 years 10 months ago
    Subgoal Discovery for Hierarchical Reinforcement Learning Using Learned Policies
    Download  www.cse.uta.edu
    Sandeep Goel, Manfred Huber
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    NIPS
    1996
    89views Information Technology» more  NIPS 1996»
    Learning Decision Theoretic Utilities through Reinforcement Learning
    13 years 10 months ago
    Learning Decision Theoretic Utilities through Reinforcement Learning
    Download  papers.cnl.salk.edu
    Magnus Stensmo, Terrence J. Sejnowski
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    NIPS
    1998
    140views Information Technology» more  NIPS 1998»
    Gradient Descent for General Reinforcement Learning
    13 years 10 months ago
    Gradient Descent for General Reinforcement Learning
    Download  www.ri.cmu.edu
    A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcementlearning algorithms. These algorithms solve a number ...
    Leemon C. Baird III, Andrew W. Moore
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    AUSAI
    2005
    Springer
    123views Artificial Intelligence» more  AUSAI 2005»
    Global Versus Local Constructive Function Approximation for On-Line Reinforcement Learning
    14 years 2 months ago
    Global Versus Local Constructive Function Approximation for On-Line Reinforcement Learning
    Download  eprints.utas.edu.au
    : In order to scale to problems with large or continuous state-spaces, reinforcement learning algorithms need to be combined with function approximation techniques. The majority of...
    Peter Vamplew, Robert Ollington
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