Title
Efficient abstraction selection in reinforcement learning
Author
van Seijen, H.
Whiteson, S.
Kester, L.
Publication year
2013
Abstract
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. Copyright © 2013 Association for the Advancement of Artificial Intelligence.
Subject
Physics & Electronics
DSS - Distributed Sensor Systems
TS - Technical Sciences
Defence Research
Informatics
Defence, Safety and Security
To reference this document use:
http://resolver.tudelft.nl/uuid:1d9ee1de-6f44-475a-baad-9f0d91d86a75
TNO identifier
489088
ISBN
9781577356301
Source
10th Symposium on Abstraction, Reformulation, and Approximation, SARA 2013, 11-12 July 2013, Leavenworth, WA, USA, 123-127
Document type
conference paper