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ICMAS
1998
15 years 5 months ago
The Moving Target Function Problem in Multi-Agent Learning
We describe a framework that can be used to model and predict the behavior of MASs with learning agents. It uses a difference equation for calculating the progression of an agent&...
José M. Vidal, Edmund H. Durfee
117
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FLAIRS
2006
15 years 5 months ago
Using Active Relocation to Aid Reinforcement Learning
We propose a new framework for aiding a reinforcement learner by allowing it to relocate, or move, to a state it selects so as to decrease the number of steps it needs to take in ...
Lilyana Mihalkova, Raymond J. Mooney
EVOW
2003
Springer
15 years 9 months ago
Exploring the T-Maze: Evolving Learning-Like Robot Behaviors Using CTRNNs
Abstract. This paper explores the capabilities of continuous time recurrent neural networks (CTRNNs) to display reinforcement learning-like abilities on a set of T-Maze and double ...
Jesper Blynel, Dario Floreano
164
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ILP
2007
Springer
15 years 10 months ago
Relational Macros for Transfer in Reinforcement Learning
We describe an application of inductive logic programming to transfer learning. Transfer learning is the use of knowledge learned in a source task to improve learning in a related ...
Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richa...
IJCM
2008
93views more  IJCM 2008»
15 years 4 months ago
A reinforced learning control using iterative error compensation for uncertain dynamical systems
This paper investigates a learning control using iterative error compensation for uncertain systems to enhance the precision of high speed, computer controlled machining process. ...
Kuei-Shu Hsu, Wen-Shyong Yu, Ming-In Ho