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» Algorithms for Inverse Reinforcement Learning
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ICML
2009
IEEE
15 years 10 months ago
Monte-Carlo simulation balancing
In this paper we introduce the first algorithms for efficiently learning a simulation policy for Monte-Carlo search. Our main idea is to optimise the balance of a simulation polic...
David Silver, Gerald Tesauro
ACL
2010
14 years 7 months ago
Reading between the Lines: Learning to Map High-Level Instructions to Commands
In this paper, we address the task of mapping high-level instructions to sequences of commands in an external environment. Processing these instructions is challenging--they posit...
S. R. K. Branavan, Luke S. Zettlemoyer, Regina Bar...
ACL
1998
14 years 11 months ago
Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email
This paper describes a novel method by which a dialogue agent can learn to choose an optimal dialogue strategy. While it is widely agreed that dialogue strategies should be formul...
Marilyn A. Walker, Jeanne Frommer, Shrikanth Naray...
IIE
2007
63views more  IIE 2007»
14 years 9 months ago
Investigation of Q-Learning in the Context of a Virtual Learning Environment
We investigate the possibility to apply a known machine learning algorithm of Q-learning in the domain of a Virtual Learning Environment (VLE). It is important in this problem doma...
Dalia Baziukaite
ALIFE
2002
14 years 9 months ago
Ant Colony Optimization and Stochastic Gradient Descent
In this paper, we study the relationship between the two techniques known as ant colony optimization (aco) and stochastic gradient descent. More precisely, we show that some empir...
Nicolas Meuleau, Marco Dorigo