Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Near-Optimal Reinforcement Learning in Polynomial Time
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
Learning the structure of Factored Markov Decision Processes in reinforcement learning problems
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning Factor Graphs in Polynomial Time and Sample Complexity
The Journal of Machine Learning Research
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
An object-oriented representation for efficient reinforcement learning
Proceedings of the 25th international conference on Machine learning
Knows what it knows: a framework for self-aware learning
Proceedings of the 25th international conference on Machine learning
Active Learning of Group-Structured Environments
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Optimistic initialization and greediness lead to polynomial time learning in factored MDPs
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
Autonomously learning an action hierarchy using a learned qualitative state representation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Models of active learning in group-structured state spaces
Information and Computation
Reinforcement Learning in Finite MDPs: PAC Analysis
The Journal of Machine Learning Research
Exploring compact reinforcement-learning representations with linear regression
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Handling ambiguous effects in action learning
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Recognizing internal states of other agents to anticipate and coordinate interactions
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
Cooperating with a markovian ad hoc teammate
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Learning potential functions and their representations for multi-task reinforcement learning
Autonomous Agents and Multi-Agent Systems
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We consider the problem of reinforcement learning in factored-state MDPs in the setting in which learning is conducted in one long trial with no resets allowed. We show how to extend existing efficient algorithms that learn the conditional probability tables of dynamic Bayesian networks (DBNs) given their structure to the case in which DBN structure is not known in advance. Our method learns the DBN structures as part of the reinforcement-learning process and provably provides an efficient learning algorithm when combined with factored Rmax.