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154
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ICML
2002
IEEE
16 years 7 months ago
Action Refinement in Reinforcement Learning by Probability Smoothing
In many reinforcement learning applications, the set of possible actions can be partitioned by the programmer into subsets of similar actions. This paper presents a technique for ...
Carles Sierra, Dídac Busquets, Ramon L&oacu...
ALT
2005
Springer
16 years 3 months ago
Defensive Universal Learning with Experts
This paper shows how universal learning can be achieved with expert advice. To this aim, we specify an experts algorithm with the following characteristics: (a) it uses only feedba...
Jan Poland, Marcus Hutter
PKDD
2009
Springer
117views Data Mining» more  PKDD 2009»
16 years 20 days ago
New Regularized Algorithms for Transductive Learning
Abstract. We propose a new graph-based label propagation algorithm for transductive learning. Each example is associated with a vertex in an undirected graph and a weighted edge be...
Partha Pratim Talukdar, Koby Crammer
181
Voted
ATAL
2007
Springer
16 years 9 days ago
Reducing the complexity of multiagent reinforcement learning
It is known that the complexity of the reinforcement learning algorithms, such as Q-learning, may be exponential in the number of environment’s states. It was shown, however, th...
Andriy Burkov, Brahim Chaib-draa
GECCO
2007
Springer
176views Optimization» more  GECCO 2007»
16 years 9 days ago
The effect of learning on life history evolution
A series of evolutionary neural network simulations are presented which explore the hypothesis that learning factors can result in the evolution of long periods of parental protec...
John A. Bullinaria