65 search results - page 2 / 13 » Gradient Descent for General Reinforcement Learning |
GECCO
2006
Springer
13 years 9 months ago
2006
Springer
This paper investigates reinforcement learning (RL) in XCS. First, it formally shows that XCS implements a method of generalized RL based on linear approximators, in which the usu...
ICML
2009
IEEE
14 years 6 months ago
2009
IEEE
We present an algorithm for finding an ssparse vector x that minimizes the squareerror y - x 2 where satisfies the restricted isometry property (RIP), with isometric constant 2s ...
JMLR
2012
11 years 7 months ago
2012
In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In ou...
NIPS
2007
13 years 6 months ago
2007
We present four new reinforcement learning algorithms based on actor-critic and natural-gradient ideas, and provide their convergence proofs. Actor-critic reinforcement learning m...
ICDM
2010
IEEE
13 years 3 months ago
2010
IEEE
On large datasets, the popular training approach has been stochastic gradient descent (SGD). This paper proposes a modification of SGD, called averaged SGD with feedback (ASF), tha...