Communications of the ACM
A model for reasoning about persistence and causation
Computational Intelligence
A Learning Criterion for Stochastic Rules
Machine Learning - Computational learning theory
Efficient distribution-free learning of probabilistic concepts
Journal of Computer and System Sciences - Special issue: 31st IEEE conference on foundations of computer science, Oct. 22–24, 1990
Journal of the ACM (JACM)
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Near-Optimal Reinforcement Learning in Polynomial Time
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
The Sample Complexity of Exploration in the Multi-Armed Bandit Problem
The Journal of Machine Learning Research
PAC model-free reinforcement learning
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
Knows what it knows: a framework for self-aware learning
Proceedings of the 25th 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
Efficient structure learning in factored-state MDPs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
A unifying framework for computational reinforcement learning theory
A unifying framework for computational reinforcement learning theory
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
Exploiting Best-Match Equations for Efficient Reinforcement Learning
The Journal of Machine Learning Research
Learning exploration strategies in model-based reinforcement learning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Cooperating with a markovian ad hoc teammate
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Exploration in relational domains for model-based reinforcement learning
The Journal of Machine Learning Research
Learning potential functions and their representations for multi-task reinforcement learning
Autonomous Agents and Multi-Agent Systems
| Hi-index | 0.00 |
The purpose of this paper is three-fold. First, we formalize and study a problem of learning probabilistic concepts in the recently proposed KWIK framework. We give details of an algorithm, known as the Adaptive k-Meteorologists Algorithm, analyze its sample-complexity upper bound, and give a matching lower bound. Second, this algorithm is used to create a new reinforcement-learning algorithm for factored-state problems that enjoys significant improvement over the previous state-of-the-art algorithm. Finally, we apply the Adaptive k-Meteorologists Algorithm to remove a limiting assumption in an existing reinforcement-learning algorithm. The effectiveness of our approaches is demonstrated empirically in a couple benchmark domains as well as a robotics navigation problem.