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EACL
2006
ACL Anthology
13 years 10 months ago
Using Reinforcement Learning to Build a Better Model of Dialogue State
Given the growing complexity of tasks that spoken dialogue systems are trying to handle, Reinforcement Learning (RL) has been increasingly used as a way of automatically learning ...
Joel R. Tetreault, Diane J. Litman
NN
2002
Springer
113views Neural Networks» more  NN 2002»
13 years 8 months ago
Control of exploitation-exploration meta-parameter in reinforcement learning
In reinforcement learning (RL), the duality between exploitation and exploration has long been an important issue. This paper presents a new method that controls the balance betwe...
Shin Ishii, Wako Yoshida, Junichiro Yoshimoto
ICRA
2009
IEEE
111views Robotics» more  ICRA 2009»
14 years 3 months ago
Model-based and model-free reinforcement learning for visual servoing
— To address the difficulty of designing a controller for complex visual-servoing tasks, two learning-based uncalibrated approaches are introduced. The first method starts by b...
Amir Massoud Farahmand, Azad Shademan, Martin J&au...
ICCBR
2009
Springer
14 years 3 months ago
Quality Enhancement Based on Reinforcement Learning and Feature Weighting for a Critiquing-Based Recommender
Personalizing the product recommendation task is a major focus of research in the area of conversational recommender systems. Conversational case-based recommender systems help use...
Maria Salamó, Sergio Escalera, Petia Radeva
NN
2007
Springer
105views Neural Networks» more  NN 2007»
13 years 8 months ago
Guiding exploration by pre-existing knowledge without modifying reward
Reinforcement learning is based on exploration of the environment and receiving reward that indicates which actions taken by the agent are good and which ones are bad. In many app...
Kary Främling