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» Optimization on a Budget: A Reinforcement Learning Approach
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NIPS
2008
11 years 3 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
SAB
2010
Springer
189views Optimization» more  SAB 2010»
10 years 11 months ago
TeXDYNA: Hierarchical Reinforcement Learning in Factored MDPs
Reinforcement learning is one of the main adaptive mechanisms that is both well documented in animal behaviour and giving rise to computational studies in animats and robots. In th...
Olga Kozlova, Olivier Sigaud, Christophe Meyer
CORR
1998
Springer
164views Education» more  CORR 1998»
11 years 1 months ago
Training Reinforcement Neurocontrollers Using the Polytope Algorithm
A new training algorithm is presented for delayed reinforcement learning problems that does not assume the existence of a critic model and employs the polytope optimization algorit...
Aristidis Likas, Isaac E. Lagaris
ICML
2010
IEEE
11 years 2 months ago
Multi-Class Pegasos on a Budget
When equipped with kernel functions, online learning algorithms are susceptible to the "curse of kernelization" that causes unbounded growth in the model size. To addres...
Zhuang Wang, Koby Crammer, Slobodan Vucetic
WSC
2007
11 years 4 months ago
Optimizing time warp simulation with reinforcement learning techniques
Adaptive Time Warp protocols in the literature are usually based on a pre-deļ¬ned analytic model of the system, expressed as a closed form function that maps system state to cont...
Jun Wang, Carl Tropper
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