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Publication
334views
15 years 9 months ago
Rollout Sampling Approximate Policy Iteration
Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schem...
Christos Dimitrakakis, Michail G. Lagoudakis
102
Voted
NN
2006
Springer
15 years 20 days ago
The misbehavior of value and the discipline of the will
Most reinforcement learning models of animal conditioning operate under the convenient, though fictive, assumption that Pavlovian conditioning concerns prediction learning whereas...
Peter Dayan, Yael Niv, Ben Seymour, Nathaniel D. D...
160
Voted

Book
392views
16 years 11 months ago
Reinforcement Learning: An Introduction
"Reinforcement learning is learning what to do how to map situations to actions so as to maximize a numerical reward signal. The learner is not told which actions to take, as ...
Richard S. Sutton, Andrew G. Barto
ATAL
2006
Springer
15 years 4 months ago
Efficient agent-based cluster ensembles
Numerous domains ranging from distributed data acquisition to knowledge reuse need to solve the cluster ensemble problem of combining multiple clusterings into a single unified cl...
Adrian K. Agogino, Kagan Tumer
95
Voted
ICML
2006
IEEE
16 years 1 months ago
Autonomous shaping: knowledge transfer in reinforcement learning
We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses prior experience on a sequence of tasks to learn a portable predictor that est...
George Konidaris, Andrew G. Barto