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    ICML
    2009
    IEEE
    172views Machine Learning» more  ICML 2009»
    Model-free reinforcement learning as mixture learning
    16 years 4 months ago
    Model-free reinforcement learning as mixture learning
    Download  user.cs.tu-berlin.de
    We cast model-free reinforcement learning as the problem of maximizing the likelihood of a probabilistic mixture model via sampling, addressing both the infinite and finite horizo...
    Nikos Vlassis, Marc Toussaint
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    ICML
    2004
    IEEE
    214views Machine Learning» more  ICML 2004»
    Apprenticeship learning via inverse reinforcement learning
    16 years 4 months ago
    Apprenticeship learning via inverse reinforcement learning
    Download  ai.stanford.edu
    We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we wa...
    Pieter Abbeel, Andrew Y. Ng
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    146
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    ICML
    2005
    IEEE
    127views Machine Learning» more  ICML 2005»
    Exploration and apprenticeship learning in reinforcement learning
    16 years 4 months ago
    Exploration and apprenticeship learning in reinforcement learning
    Download  ai.stanford.edu
    We consider reinforcement learning in systems with unknown dynamics. Algorithms such as E3 (Kearns and Singh, 2002) learn near-optimal policies by using "exploration policies...
    Pieter Abbeel, Andrew Y. Ng
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    139
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    SIGGRAPH
    2010
    ACM
    295views Computer Graphics» more  SIGGRAPH 2010»
    Learning behavior styles with inverse reinforcement learning
    15 years 8 months ago
    Learning behavior styles with inverse reinforcement learning
    Download  grail.cs.washington.edu
    We present a method for inferring the behavior styles of character controllers from a small set of examples. We show that a rich set of behavior variations can be captured by dete...
    Seong Jae Lee, Zoran Popovic
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    123
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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
    16 years 4 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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