92 search results - page 5 / 19 » A General Convergence Method for Reinforcement Learning in t... |
ICML
1995
IEEE
14 years 7 months ago
1995
IEEE
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to the optimal solution when used with lookup tables. It is shown, however, that ...
ICAC
2008
IEEE
14 years 1 months ago
2008
IEEE
—Large scale production grids are an important case for autonomic computing. They follow a mutualization paradigm: decision-making (human or automatic) is distributed and largely...
EUROCAST
2007
Springer
14 years 27 days ago
2007
Springer
Abstract a paradigm of modern Machine Learning (ML) which uses rewards and punishments to guide the learning process. One of the central ideas of RL is learning by “direct-online...
ROBOCUP
2000
Springer
13 years 10 months ago
2000
Springer
Q-learning, a most widely used reinforcement learning method, normally needs well-defined quantized state and action spaces to converge. This makes it difficult to be applied to re...
ICCBR
2010
Springer
13 years 10 months ago
2010
Springer
In this paper we present an approach for reducing the memory footprint requirement of temporal difference methods in which the set of states is finite. We use case-based generaliza...