Layered control architectures in robots and vertebrates
Adaptive Behavior
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Machine Learning
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Using relative novelty to identify useful temporal abstractions in reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Constructing the basic Umwelt of artificial agents: an information-theoretic approach
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
All else being equal be empowered
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Adaptivity on the robot brain architecture level using reinforcement learning
Robot Soccer World Cup XV
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Hierarchical structuring of behaviour is prevalent in natural and artificial agents and can be shown to be useful for learning and performing tasks. To progress systematic understanding of these benefits we study the effect of hierarchical architectures on the required information processing capability of an optimally acting agent. We show that an information-theoretical approach provides important insights into why factored and layered behaviour structures are beneficial.