125 search results - page 10 / 25 » Reinforcement Learning in Continuous Time and Space |
ROBOCUP
2000
Springer
14 years 4 days 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...
AUSAI
2004
Springer
14 years 2 months ago
2004
Springer
In this paper, we propose a dynamic allocation method of basis functions, an Allocation/Elimination Gaussian Softmax Basis Function Network (AE-GSBFN), that is used in reinforcemen...
UAI
2008
13 years 10 months ago
2008
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinfor...
ESANN
2007
13 years 10 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...
ICRA
1995
IEEE
14 years 4 days ago
1995
IEEE
This paper presents a method of vision-based reinforcement learning by which a robot learns to shoot a ball into a goal, and discusses several issues in applying the reinforcement...