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efficient abstraction selection

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Efficient Abstraction Selection in Reinforcement Learning (Extended Abstract)

Seijen, Harm van (University of Alberta) | Whiteson, Shimon (University of Amsterdam) | Kester, Leon (TNO)

AAAI ConferencesJul-5-2013

This paper introduces a novel approach for abstraction selection in reinforcement learning problems modelled as factored Markov decision processes (MDPs), for which a state is described via a set of state components. In abstraction selection, an agent must choose an abstraction from a set of candidate abstractions, each build up from a different combination of state components.

efficient abstraction selection, extended abstract, reinforcement learning

AAAI Conferences

Tenth Symposium of Abstraction, Reformulation, and Approximation

Technology: Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.60)

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