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Shaping Proto-Value Functions via Rewards
November 27, 2015 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Chandrashekar Lakshmi Narayanan, Raj Kumar Maity, Shalabh Bhatnagar
arXiv ID
1511.08589
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.LG
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
In this paper, we combine task-dependent reward shaping and task-independent proto-value functions to obtain reward dependent proto-value functions (RPVFs). In constructing the RPVFs we are making use of the immediate rewards which are available during the sampling phase but are not used in the PVF construction. We show via experiments that learning with an RPVF based representation is better than learning with just reward shaping or PVFs. In particular, when the state space is symmetrical and the rewards are asymmetrical, the RPVF capture the asymmetry better than the PVFs.
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