Associative Reinforcement Learning: Functions in k-DNF
Machine Learning
Learning in graphical models
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Complexity of finite-horizon Markov decision process problems
Journal of the ACM (JACM)
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Exploration Control in Reinforcement Learning using Optimistic Model Selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Near-Optimal Reinforcement Learning in Polynominal Time
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Bayesian Framework for Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
PEGASUS: A policy search method for large MDPs and POMDPs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Optimal learning: computational procedures for bayes-adaptive markov decision processes
Optimal learning: computational procedures for bayes-adaptive markov decision processes
Policy search using paired comparisons
The Journal of Machine Learning Research
Nonapproximability results for partially observable Markov decision processes
Journal of Artificial Intelligence Research
A sparse sampling algorithm for near-optimal planning in large Markov decision processes
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
R-MAX: a general polynomial time algorithm for near-optimal reinforcement learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Model based Bayesian exploration
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Reinforcement learning for active model selection
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
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
Near-Bayesian exploration in polynomial time
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Action selection in Bayesian reinforcement learning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Monte Carlo sampling methods for approximating interactive POMDPs
Journal of Artificial Intelligence Research
Automatic gait optimization with Gaussian process regression
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using linear programming for Bayesian exploration in Markov decision processes
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Bayesian reinforcement learning in continuous pomdps with Gaussian processes
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
A Bayesian sampling approach to exploration in reinforcement learning
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
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
Solving non-stationary bandit problems by random sampling from sibling Kalman filters
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
A Monte-Carlo AIXI approximation
Journal of Artificial Intelligence Research
A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes
The Journal of Machine Learning Research
Learning form experience: a bayesian network based reinforcement learning approach
ICICA'11 Proceedings of the Second international conference on Information Computing and Applications
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
New algorithms for budgeted learning
Machine Learning
Hybrid POMDP based evolutionary adaptive framework for efficient visual tracking algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Testing probabilistic equivalence through Reinforcement Learning
Information and Computation
Prior-free exploration bonus for and beyond near bayes-optimal behavior
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial 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 an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration versus exploitation tradeoff. Our approach combines sparse sampling with Bayesian exploration to achieve improved decision making while controlling computational cost. The idea is to grow a sparse lookahead tree, intelligently, by exploiting information in a Bayesian posterior---rather than enumerate action branches (standard sparse sampling) or compensate myopically (value of perfect information). The outcome is a flexible, practical technique for improving action selection in simple reinforcement learning scenarios.