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» Opposition-Based Reinforcement Learning
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119
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CEEMAS
2003
Springer
15 years 6 months ago
On a Dynamical Analysis of Reinforcement Learning in Games: Emergence of Occam's Razor
Modeling learning agents in the context of Multi-agent Systems requires an adequate understanding of their dynamic behaviour. Usually, these agents are modeled similar to the diļ¬...
Karl Tuyls, Katja Verbeeck, Sam Maes
92
Voted
ITS
2010
Springer
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15 years 4 months ago
Do Micro-Level Tutorial Decisions Matter: Applying Reinforcement Learning to Induce Pedagogical Tutorial Tactics
Pedagogical tutorial tactics are policies for a tutor to decide the next action when there are multiple actions available. When the contents were controlled so as to be the same, l...
Min Chi, Kurt VanLehn, Diane J. Litman
FLAIRS
2007
15 years 3 months ago
Guiding Inference with Policy Search Reinforcement Learning
Symbolic reasoning is a well understood and effective approach to handling reasoning over formally represented knowledge; however, simple symbolic inference systems necessarily sl...
Matthew E. Taylor, Cynthia Matuszek, Pace Reagan S...
103
Voted
ECAI
2008
Springer
15 years 2 months ago
Belief revision with reinforcement learning for interactive object recognition
From a conceptual point of view, belief revision and learning are quite similar. Both methods change the belief state of an intelligent agent by processing incoming information. Ho...
Thomas Leopold, Gabriele Kern-Isberner, Gabriele P...
102
Voted
AAAI
2010
15 years 2 months ago
Integrating Sample-Based Planning and Model-Based Reinforcement Learning
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...