82 search results - page 8 / 17 » Practical Reinforcement Learning in Continuous Spaces |
FLAIRS
2008
13 years 11 months ago
2008
Although several researchers have integrated methods for reinforcement learning (RL) with case-based reasoning (CBR) to model continuous action spaces, existing integrations typic...
SGAI
2004
Springer
14 years 1 months ago
2004
Springer
We introduce a new class of Reinforcement Learning algorithms designed to operate in perceptual spaces containing images. They work by classifying the percepts using a computer vi...
ICMLA
2008
13 years 10 months ago
2008
In reinforcement learning, it is a common practice to map the state(-action) space to a different one using basis functions. This transformation aims to represent the input data i...
ICML
2001
IEEE
14 years 9 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...
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...