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Automatic landmark discovery for learning agents under partial observability
Date
2019-08-02
Author
DEMİR, ALPER
Cilden, Erkin
Polat, Faruk
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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In the reinforcement learning context, a landmark is a compact information which uniquely couples a state, for problems with hidden states. Landmarks are shown to support finding good memoryless policies for Partially Observable Markov Decision Processes (POMDP) which contain at least one landmark. SarsaLandmark, as an adaptation of Sarsa(lambda), is known to promise a better learning performance with the assumption that all landmarks of the problem are known in advance.
Subject Keywords
Software
,
Artificial Intelligence
URI
https://hdl.handle.net/11511/39370
Journal
KNOWLEDGE ENGINEERING REVIEW
DOI
https://doi.org/10.1017/s026988891900002x
Collections
Department of Computer Engineering, Article