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ICML
2003
IEEE
14 years 9 months ago
Exploration in Metric State Spaces
We present metric?? , a provably near-optimal algorithm for reinforcement learning in Markov decision processes in which there is a natural metric on the state space that allows t...
Sham Kakade, Michael J. Kearns, John Langford
IWLCS
2005
Springer
14 years 2 months ago
Counter Example for Q-Bucket-Brigade Under Prediction Problem
Aiming to clarify the convergence or divergence conditions for Learning Classifier System (LCS), this paper explores: (1) an extreme condition where the reinforcement process of ...
Atsushi Wada, Keiki Takadama, Katsunori Shimohara
ICASSP
2011
IEEE
13 years 9 days ago
Reinforcement learning for energy-efficient wireless transmission
We consider the problem of energy-efficient point-to-point transmission of delay-sensitive data (e.g. multimedia data) over a fading channel. We propose a rigorous and unified fra...
Nicholas Mastronarde, Mihaela van der Schaar
ECML
2007
Springer
14 years 15 days ago
Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass
ML
2002
ACM
121views Machine Learning» more  ML 2002»
13 years 8 months ago
Near-Optimal Reinforcement Learning in Polynomial Time
We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decisio...
Michael J. Kearns, Satinder P. Singh