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Selecting Near-Optimal Approximate State Representations in
  Reinforcement Learning

Selecting Near-Optimal Approximate State Representations in Reinforcement Learning

12 May 2014
R. Ortner
Odalric-Ambrym Maillard
D. Ryabko
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Papers citing "Selecting Near-Optimal Approximate State Representations in Reinforcement Learning"

5 / 5 papers shown
Title
Optimal Regret Bounds for Selecting the State Representation in
  Reinforcement Learning
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning
Odalric-Ambrym Maillard
P. Nguyen
R. Ortner
D. Ryabko
65
30
0
11 Feb 2013
Selecting the State-Representation in Reinforcement Learning
Selecting the State-Representation in Reinforcement Learning
Odalric-Ambrym Maillard
Rémi Munos
D. Ryabko
54
40
0
11 Feb 2013
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning
R. Ortner
D. Ryabko
OffRL
69
85
0
11 Feb 2013
REGAL: A Regularization based Algorithm for Reinforcement Learning in
  Weakly Communicating MDPs
REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs
Peter L. Bartlett
Ambuj Tewari
71
280
0
09 May 2012
Feature Reinforcement Learning: Part I: Unstructured MDPs
Feature Reinforcement Learning: Part I: Unstructured MDPs
Marcus Hutter
72
67
0
09 Jun 2009
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