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    » Algorithms for Inverse Reinforcement Learning
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    AAAI
    1993
    107views Intelligent Agents» more  AAAI 1993»
    Complexity Analysis of Real-Time Reinforcement Learning
    13 years 9 months ago
    Complexity Analysis of Real-Time Reinforcement Learning
    Download  www.ri.cmu.edu
    This paper analyzes the complexity of on-line reinforcement learning algorithms, namely asynchronous realtime versions of Q-learning and value-iteration, applied to the problem of...
    Sven Koenig, Reid G. Simmons
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    ICML
    2009
    IEEE
    160views Machine Learning» more  ICML 2009»
    The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning
    14 years 8 months ago
    The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning
    Download  www.research.rutgers.edu
    The purpose of this paper is three-fold. First, we formalize and study a problem of learning probabilistic concepts in the recently proposed KWIK framework. We give details of an ...
    Carlos Diuk, Lihong Li, Bethany R. Leffler
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    ICAART
    2010
    INSTICC
    509views Intelligent Agents» more  ICAART 2010»
    Complexity of Stochastic Branch and Bound Methods for Belief Tree Search in Bayesian Reinforcement Learning
    14 years 4 months ago
    Complexity of Stochastic Branch and Bound Methods for Belief Tree Search in Bayesian Reinforcement Learning
    Download  arxiv.org
    There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most...
    Christos Dimitrakakis
    posted by olethros
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    IJCAI
    2007
    179views Artificial Intelligence» more  IJCAI 2007»
    Deictic Option Schemas
    13 years 9 months ago
    Deictic Option Schemas
    Download  www.ijcai.org
    Deictic representation is a representational paradigm, based on selective attention and pointers, that allows an agent to learn and reason about rich complex environments. In this...
    Balaraman Ravindran, Andrew G. Barto, Vimal Mathew
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    NETWORKING
    2007
    110views Computer Networks» more  NETWORKING 2007»
    Reinforcement Learning-Based Load Shared Sequential Routing
    13 years 9 months ago
    Reinforcement Learning-Based Load Shared Sequential Routing
    Download  www.ece.mcgill.ca
    We consider event dependent routing algorithms for on-line explicit source routing in MPLS networks. The proposed methods are based on load shared sequential routing in which load ...
    Fariba Heidari, Shie Mannor, Lorne Mason
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