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» Batch Reinforcement Learning with State Importance
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LWA
2007
14 years 11 months ago
Towards Learning User-Adaptive State Models in a Conversational Recommender System
Typical conversational recommender systems support interactive strategies that are hard-coded in advance and followed rigidly during a recommendation session. In fact, Reinforceme...
Tariq Mahmood, Francesco Ricci
BIOADIT
2004
Springer
15 years 2 months ago
Autonomous Acquisition of the Meaning of Sensory States Through Sensory-Invariance Driven Action
Abstract. How can artificial or natural agents autonomously gain understanding of its own internal (sensory) state? This is an important question not just for physically embodied ...
Yoonsuck Choe, S. Kumar Bhamidipati
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
ECAI
2006
Springer
15 years 1 months ago
Using Emotions for Behaviour-Selection Learning
Emotions play a very important role in human behaviour and social interaction. In this paper we present a control architecture which uses emotions in the behaviour selection proces...
Maria Malfaz, Miguel Angel Salichs
ICML
2000
IEEE
15 years 10 months ago
Eligibility Traces for Off-Policy Policy Evaluation
Eligibility traces have been shown to speed reinforcement learning, to make it more robust to hidden states, and to provide a link between Monte Carlo and temporal-difference meth...
Doina Precup, Richard S. Sutton, Satinder P. Singh