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ICML
2009
IEEE
14 years 4 months ago
Near-Bayesian exploration in polynomial time
We consider the exploration/exploitation problem in reinforcement learning (RL). The Bayesian approach to model-based RL offers an elegant solution to this problem, by considering...
J. Zico Kolter, Andrew Y. Ng
ML
2002
ACM
121views Machine Learning» more  ML 2002»
13 years 3 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
TCS
2008
13 years 3 months ago
Nondeterministic polynomial time factoring in the tile assembly model
Formalized study of self-assembly has led to the definition of the tile assembly model, Previously I presented ways to compute arithmetic functions, such as addition and multiplic...
Yuriy Brun
IJCAI
2001
13 years 5 months ago
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
Ronen I. Brafman, Moshe Tennenholtz
FOCS
1994
IEEE
13 years 7 months ago
The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs
We show that two cooperating robots can learn exactly any strongly-connected directed graph with n indistinguishable nodes in expected time polynomial in n. We introduce a new typ...
Michael A. Bender, Donna K. Slonim