181 search results - page 30 / 37 » State Space Reduction For Hierarchical Reinforcement Learnin... |
ESANN
2007
13 years 11 months ago
2007
This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in di...
IFM
2009
Springer
14 years 4 months ago
2009
Springer
We present the new technique of dynamic path reduction (DPR), which allows one to prune redundant paths from the state space of a program under verification. DPR is a very general...
ICML
2001
IEEE
14 years 10 months ago
2001
IEEE
Direct policy search is a practical way to solve reinforcement learning problems involving continuous state and action spaces. The goal becomes finding policy parameters that maxi...
ICCS
1993
Springer
14 years 1 months ago
1993
Springer
Adaptive predictive search (APS), is a learning system framework, which given little initial domain knowledge, increases its decision-making abilities in complex problems domains....
BMVC
2010
13 years 7 months ago
2010
We present a framework for the reduction of dimensionality of a data set via manifold learning. Using the building blocks of local hyperplanes we show how a global manifold can be...