Statecharts: A visual formalism for complex systems
Science of Computer Programming
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Learning to solve multiple goals
Learning to solve multiple goals
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Discovering Hierarchy in Reinforcement Learning with HEXQ
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Coarticulation: an approach for generating concurrent plans in Markov decision processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Workshop summary: Abstraction in reinforcement learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
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In this paper the notion of a partial-order plan is extended to task-hierarchies. We introduce the concept of a partial-order task-hierarchy that decomposes a problem using multi-tasking actions. We go further and show how a problem can be automatically decomposed into a partial-order task-hierarchy, and solved using hierarchical reinforcement learning. The problem structure determines the reduction in memory requirements and learning time.