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
Temporal difference learning and TD-Gammon
Communications of the ACM
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Efficient Reinforcement Learning in Factored MDPs
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
Improving action selection in MDP's via knowledge transfer
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Online kernel selection for Bayesian reinforcement learning
Proceedings of the 25th international conference on Machine learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Generalized model learning for reinforcement learning in factored domains
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Potential-based shaping in model-based reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A case study on the critical role of geometric regularity in machine learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Discovering Abstract Concepts to Aid Cross-Map Transfer for a Learning Agent
DS '09 Proceedings of the 12th International Conference on Discovery Science
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Provably Efficient Learning with Typed Parametric Models
The Journal of Machine Learning Research
Reinforcement Learning in Finite MDPs: PAC Analysis
The Journal of Machine Learning Research
A Bayesian sampling approach to exploration in reinforcement learning
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
V-MAX: tempered optimism for better PAC reinforcement learning
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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Realistic domains for learning possess regularities that make it possible to generalize experience across related states. This paper explores an environment-modeling framework that represents transitions as state-independent outcomes that are common to all states that share the same type. We analyze a set of novel learning problems that arise in this framework, providing lower and upper bounds. We single out one particular variant of practical interest and provide an efficient algorithm and experimental results in both simulated and robotic environments.