548 search results - page 18 / 110 » A New Way to Introduce Knowledge into Reinforcement Learning |
GECCO
2005
Springer
15 years 5 months ago
2005
Springer
Recurrent neural networks are theoretically capable of learning complex temporal sequences, but training them through gradient-descent is too slow and unstable for practical use i...
HICSS
2006
IEEE
15 years 5 months ago
2006
IEEE
The aims of this study are: (1) to examine to what extent critical care and advanced practice nurses’ participation in an online listserv constituted a community of practice, an...
NIPS
2007
15 years 1 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...
IWANN
1999
Springer
15 years 4 months ago
1999
Springer
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
108
Voted
IJCAI
2001
15 years 1 months ago
2001
Using a distributed algorithm rather than a centralized one can be extremely beneficial in large search problems. In addition, the incorporation of machine learning techniques lik...