29 search results - page 4 / 6 » Action Refinement in Reinforcement Learning by Probability S... |
ROBOCUP
2000
Springer
13 years 11 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...
ATAL
2008
Springer
13 years 9 months ago
2008
Springer
Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents' decisions. Only a subset of these MARL algorithms both do not require agent...
ILP
2007
Springer
14 years 1 months ago
2007
Springer
Abstract. Many reinforcement learning domains are highly relational. While traditional temporal-difference methods can be applied to these domains, they are limited in their capaci...
BROADNETS
2004
IEEE
13 years 11 months ago
2004
IEEE
The scarcity and large fluctuations of link bandwidth in wireless networks have motivated the development of adaptive multimedia services in mobile communication networks, where i...
ICML
2001
IEEE
14 years 8 months ago
2001
IEEE
We call data weakly labeled if it has no exact label but rather a numerical indication of correctness of the label "guessed" by the learning algorithm - a situation comm...