David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
Learn more about PhilPapers
chical reinforcement learning that does not rely on a pri ori hierarchical structures Thus the approach deals with a more di cult problem compared with existing work It in volves learning to segment sequences to create hierarchical structures based on reinforcement received during task ex ecution with di erent levels of control communicating with each other through sharing reinforcement estimates obtained by each others The algorithm segments sequences to re duce non Markovian temporal dependencies to facilitate the learning of the overall task Initial experiments demon strated the basic promise of the approach..
|Keywords||No keywords specified (fix it)|
No categories specified
(categorize this paper)
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Peter F. Dominey (1997). Reducing Problem Complexity by Analogical Transfer. Behavioral and Brain Sciences 20 (1):71-72.
C. P. Schnorr & P. Fuchs (1977). General Random Sequences and Learnable Sequences. Journal of Symbolic Logic 42 (3):329-340.
Reiko Yakushijin & Robert A. Jacobs (2011). Are People Successful at Learning Sequences of Actions on a Perceptual Matching Task? Cognitive Science 35 (5):939-962.
Added to index2009-06-13
Total downloads6 ( #532,620 of 1,902,696 )
Recent downloads (6 months)1 ( #452,252 of 1,902,696 )
How can I increase my downloads?