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    ICML
    2009
    IEEE
    172views Machine Learning» more  ICML 2009»
    Model-free reinforcement learning as mixture learning
    15 years 10 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
    15 years 10 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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    ICML
    2005
    IEEE
    127views Machine Learning» more  ICML 2005»
    Exploration and apprenticeship learning in reinforcement learning
    15 years 10 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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    SIGGRAPH
    2010
    ACM
    295views Computer Graphics» more  SIGGRAPH 2010»
    Learning behavior styles with inverse reinforcement learning
    15 years 2 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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    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
    15 years 10 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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