Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
O-Plan: the open planning architecture
Artificial Intelligence
Elements of information theory
Elements of information theory
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Reinforcement learning with hierarchies of machines
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Multi-time models for temporally abstract planning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Composing Functions to Speed up Reinforcement Learning in a Changing World
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Theoretical Results on Reinforcement Learning with Temporally Abstract Options
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The MAXQ Method for Hierarchical Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Multi-Value-Functions: Efficient Automatic Action Hierarchies for Multiple Goal MDPs
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Between MOPs and Semi-MOP: Learning, Planning & Representing Knowledge at Multiple Temporal Scales
Between MOPs and Semi-MOP: Learning, Planning & Representing Knowledge at Multiple Temporal Scales
Hierarchical control and learning for markov decision processes
Hierarchical control and learning for markov decision processes
Hierarchical solution of Markov decision processes using macro-actions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Accelerating autonomous learning by using heuristic selection of actions
Journal of Heuristics
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Learning Representation and Control in Markov Decision Processes: New Frontiers
Foundations and Trends® in Machine Learning
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Learning potential functions and their representations for multi-task reinforcement learning
Autonomous Agents and Multi-Agent Systems
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Solving in an efficient manner many different optimal control tasks within the same underlying environment requires decomposing the environment into its computationally elemental fragments. We suggest how to find fragmentations using unsupervised, mixture model, learning methods on data derived from optimal value functions for multiple tasks, and show that these fragmentations are in accord with observable structure in the environments. Further, we present evidence that such fragments can be of use in a practical reinforcement learning context, by facilitating online, actor-critic learning of multiple goals MDPs.