๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Reinforcement learning strategies for sequential action learning

โœ Scribed by Fermin, Alan; Takehiko, Yoshida; Tanaka, Saori; Ito, Makoto; Yoshimoto, Junichiro; Doya, Kenji


Book ID
122599655
Publisher
Elsevier Science
Year
2009
Tongue
English
Weight
76 KB
Volume
65
Category
Article
ISSN
0168-0102

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Learning competitive pricing strategies
โœ Erich Kutschinski; Thomas Uthmann; Daniel Polani ๐Ÿ“‚ Article ๐Ÿ“… 2003 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 218 KB

In electronic marketplaces automated and dynamic pricing is becoming increasingly popular. Agents that perform this task can improve themselves by learning from past observations, possibly using reinforcement learning techniques. Co-learning of several adaptive agents against each other may lead to

Learning action strategies for planning
โœ Roni Khardon ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 246 KB

This paper reports on experiments where techniques of supervised machine learning are applied to the problem of planning. The input to the learning algorithm is composed of a description of a planning domain, planning problems in this domain, and solutions for them. The output is an efficient algori

Reinforcement learning using multiple ac
โœ Nakama, Hayato; Asano, Tsubasa; Yamada, Satoshi ๐Ÿ“‚ Article ๐Ÿ“… 2010 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 65 KB