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CVPR
2012
IEEE
12 years 17 days ago
RALF: A reinforced active learning formulation for object class recognition
Active learning aims to reduce the amount of labels required for classification. The main difficulty is to find a good trade-off between exploration and exploitation of the lab...
Sandra Ebert, Mario Fritz, Bernt Schiele
NIPS
2008
13 years 11 months ago
Optimization on a Budget: A Reinforcement Learning Approach
Many popular optimization algorithms, like the Levenberg-Marquardt algorithm (LMA), use heuristic-based "controllers" that modulate the behavior of the optimizer during ...
Paul Ruvolo, Ian R. Fasel, Javier R. Movellan
ICML
2002
IEEE
14 years 11 months ago
Algorithm-Directed Exploration for Model-Based Reinforcement Learning in Factored MDPs
One of the central challenges in reinforcement learning is to balance the exploration/exploitation tradeoff while scaling up to large problems. Although model-based reinforcement ...
Carlos Guestrin, Relu Patrascu, Dale Schuurmans
ATAL
2007
Springer
14 years 4 months ago
Model-based function approximation in reinforcement learning
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
Nicholas K. Jong, Peter Stone
ML
1998
ACM
101views Machine Learning» more  ML 1998»
13 years 9 months ago
Elevator Group Control Using Multiple Reinforcement Learning Agents
Recent algorithmic and theoretical advances in reinforcement learning (RL) have attracted widespread interest. RL algorithmshave appeared that approximatedynamic programming on an ...
Robert H. Crites, Andrew G. Barto