Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
A Bayesian Framework for Reinforcement Learning
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
Bayesian sparse sampling for on-line reward optimization
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
Multi-task reinforcement learning: a hierarchical Bayesian approach
Proceedings of the 24th international conference on Machine learning
Efficient reinforcement learning with relocatable action models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Globally Optimal Multi-agent Reinforcement Learning Parameters in Distributed Task Assignment
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Reinforcement Learning in Finite MDPs: PAC Analysis
The Journal of Machine Learning Research
Smarter sampling in model-based Bayesian reinforcement learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Reducing reinforcement learning to KWIK online regression
Annals of Mathematics and Artificial Intelligence
A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes
The Journal of Machine Learning Research
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Robust bayesian reinforcement learning through tight lower bounds
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Optimistic Bayesian sampling in contextual-bandit problems
The Journal of Machine Learning Research
Bayes-optimal reinforcement learning for discrete uncertainty domains
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Learning agents with evolving hypothesis classes
AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
Linear Bayesian reinforcement learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Monte-Carlo tree search for Bayesian reinforcement learning
Applied Intelligence
Scalable and efficient bayes-adaptive reinforcement learning based on monte-carlo tree search
Journal of Artificial Intelligence Research
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We present a modular approach to reinforcement learning that uses a Bayesian representation of the uncertainty over models. The approach, BOSS (Best of Sampled Set), drives exploration by sampling multiple models from the posterior and selecting actions optimistically. It extends previous work by providing a rule for deciding when to re-sample and how to combine the models. We show that our algorithm achieves near-optimal reward with high probability with a sample complexity that is low relative to the speed at which the posterior distribution converges during learning. We demonstrate that BOSS performs quite favorably compared to state-of-the-art reinforcement-learning approaches and illustrate its flexibility by pairing it with a non-parametric model that generalizes across states.