Communications of the ACM
Introduction to algorithms
Technical Note: \cal Q-Learning
Machine Learning
Efficient reinforcement learning
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
An Upper Bound on the Loss from Approximate Optimal-Value Functions
Machine Learning
Machine Learning - Special issue on reinforcement learning
The asymptotic convergence-rate of Q-learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Finite-sample convergence rates for Q-learning and indirect algorithms
Proceedings of the 1998 conference on Advances in neural information processing systems II
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
Machine Learning
Machine Learning
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Rates of Convergence for Variable Resolution Schemes in Optimal Control
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
The Journal of Machine Learning Research
The Sample Complexity of Exploration in the Multi-Armed Bandit Problem
The Journal of Machine Learning Research
A theoretical analysis of Model-Based Interval Estimation
ICML '05 Proceedings of the 22nd international conference on Machine learning
An analytic solution to discrete Bayesian reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
PAC model-free reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
An analysis of model-based Interval Estimation for Markov Decision Processes
Journal of Computer and System Sciences
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
Efficient reinforcement learning with relocatable action models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Efficient structure learning in factored-state MDPs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Potential-based shaping in model-based reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Efficient reinforcement learning in factored MDPs
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Learning to act using real-time dynamic programming
Artificial Intelligence
A Bayesian sampling approach to exploration in reinforcement learning
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Exploring compact reinforcement-learning representations with linear regression
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
Reducing reinforcement learning to KWIK online regression
Annals of Mathematics and Artificial Intelligence
Extended spatial and temporal learning scale in reinforcement learning
CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
A Monte-Carlo AIXI approximation
Journal of Artificial Intelligence Research
Robust bayesian reinforcement learning through tight lower bounds
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
V-MAX: tempered optimism for better PAC reinforcement learning
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
PAC bounds for discounted MDPs
ALT'12 Proceedings of the 23rd international conference on Algorithmic Learning Theory
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
The Journal of Machine Learning Research
Exploration in relational domains for model-based reinforcement learning
The Journal of Machine Learning Research
Scalable and efficient bayes-adaptive reinforcement learning based on monte-carlo tree search
Journal of Artificial Intelligence Research
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We study the problem of learning near-optimal behavior in finite Markov Decision Processes (MDPs) with a polynomial number of samples. These "PAC-MDP" algorithms include the well-known E3 and R-MAX algorithms as well as the more recent Delayed Q-learning algorithm. We summarize the current state-of-the-art by presenting bounds for the problem in a unified theoretical framework. A more refined analysis for upper and lower bounds is presented to yield insight into the differences between the model-free Delayed Q-learning and the model-based R-MAX.