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Journal of Complexity
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COLT '94 Proceedings of the seventh annual conference on Computational learning theory
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An introduction to computational learning theory
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
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Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Near-Optimal Reinforcement Learning in Polynomial Time
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Optimal learning: computational procedures for bayes-adaptive markov decision processes
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ICML '06 Proceedings of the 23rd international conference on Machine learning
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Near-Bayesian exploration in polynomial time
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Online exploration in least-squares policy iteration
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Journal of Artificial Intelligence Research
Reinforcement Learning in Finite MDPs: PAC Analysis
The Journal of Machine Learning Research
Model-based exploration in continuous state spaces
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
A Bayesian sampling approach to exploration in reinforcement learning
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
A unifying framework for computational reinforcement learning theory
A unifying framework for computational reinforcement learning theory
Near-optimal Regret Bounds for Reinforcement Learning
The Journal of Machine Learning Research
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One of the key problems in reinforcement learning (RL) is balancing exploration and exploitation. Another is learning and acting in large Markov decision processes (MDPs) where compact function approximation has to be used. This paper introduces REKWIRE, a provably efficient, model-free algorithm for finite-horizon RL problems with value function approximation (VFA) that addresses the exploration-exploitation tradeoff in a principled way. The crucial element of this algorithm is a reduction of RL to online regression in the recently proposed KWIK learning model. We show that, if the KWIK online regression problem can be solved efficiently, then the sample complexity of exploration of REKWIRE is polynomial. Therefore, the reduction suggests a new and sound direction to tackle general RL problems. The efficiency of our algorithm is verified on a set of proof-of-concept experiments where popular, ad hoc exploration approaches fail.