Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Approximately Optimal Approximate Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Near-Optimal Reinforcement Learning in Polynominal Time
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Predictive state representations: a new theory for modeling dynamical systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Sensitive error correcting output codes
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Rollout sampling approximate policy iteration
Machine Learning
Algorithms and Bounds for Rollout Sampling Approximate Policy Iteration
Recent Advances in Reinforcement Learning
Search-based structured prediction
Machine Learning
Bounds for multistage stochastic programs using supervised learning strategies
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Learning from demonstration using MDP induced metrics
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Reducing reinforcement learning to KWIK online regression
Annals of Mathematics and Artificial Intelligence
On-line classification of data streams with missing values based on reinforcement learning
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Reinforcement learning and apprenticeship learning for robotic control
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Besting the quiz master: crowdsourcing incremental classification games
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Reinforcement learning in robotics: A survey
International Journal of Robotics Research
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We prove a quantitative connection between the expected sum of rewards of a policy and binary classification performance on created subproblems. This connection holds without any unobservable assumptions (no assumption of independence, small mixing time, fully observable states, or even hidden states) and the resulting statement is independent of the number of states or actions. The statement is critically dependent on the size of the rewards and prediction performance of the created classifiers.We also provide some general guidelines for obtaining good classification performance on the created subproblems. In particular, we discuss possible methods for generating training examples for a classifier learning algorithm.