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    ATAL
    2009
    Springer
    77views Intelligent Agents» more  ATAL 2009»
    Learning with whom to communicate using relational reinforcement learning
    16 years 1 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
    15 years 7 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
    15 years 7 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
    15 years 7 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
    16 years 22 hour 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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