Abstract
In the paper, an adaptive learning approach of integrating evolution fuzzy-neural networks and Q-learning is developed so that a mobile robot can adapt itself to a real and complex environment. Specifically, based on Q-value and an evolution method that adjusts their parameter values of the fuzzy-neural networks, the mobile robot evolves better strategies to adapt to the environment. However, in most studies of evolution learning, the learning of mobile robots often requires a simulator and an enormous amount of evolution time so as to perform a task. Therefore, we are to integrate Q-learning into the evolution fuzzy-neural networks to avoid the requirement of the simulator. Experiment results of a mobile robot illustrate the performance of the proposed approach.
| Original language | English |
|---|---|
| Title of host publication | 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008 |
| Pages | 1902-1906 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2008 Nov 7 |
| Event | 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008 - Hong Kong, China Duration: 2008 Jun 1 → 2008 Jun 6 |
Other
| Other | 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008 |
|---|---|
| Country | China |
| City | Hong Kong |
| Period | 08/6/1 → 08/6/6 |
Fingerprint
ASJC Scopus subject areas
- Theoretical Computer Science
- Software
- Artificial Intelligence
- Applied Mathematics
Cite this
Adaptive learning approach of integrating evolution fuzzy-neural networks and Q-learning for mobile robots. / Zhon, Hong Jian; Hsieh, Wei Min; Leu, Yih-Guang; Hong, Chin-Ming.
2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008. 2008. p. 1902-1906 4630629.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Adaptive learning approach of integrating evolution fuzzy-neural networks and Q-learning for mobile robots
AU - Zhon, Hong Jian
AU - Hsieh, Wei Min
AU - Leu, Yih-Guang
AU - Hong, Chin-Ming
PY - 2008/11/7
Y1 - 2008/11/7
N2 - In the paper, an adaptive learning approach of integrating evolution fuzzy-neural networks and Q-learning is developed so that a mobile robot can adapt itself to a real and complex environment. Specifically, based on Q-value and an evolution method that adjusts their parameter values of the fuzzy-neural networks, the mobile robot evolves better strategies to adapt to the environment. However, in most studies of evolution learning, the learning of mobile robots often requires a simulator and an enormous amount of evolution time so as to perform a task. Therefore, we are to integrate Q-learning into the evolution fuzzy-neural networks to avoid the requirement of the simulator. Experiment results of a mobile robot illustrate the performance of the proposed approach.
AB - In the paper, an adaptive learning approach of integrating evolution fuzzy-neural networks and Q-learning is developed so that a mobile robot can adapt itself to a real and complex environment. Specifically, based on Q-value and an evolution method that adjusts their parameter values of the fuzzy-neural networks, the mobile robot evolves better strategies to adapt to the environment. However, in most studies of evolution learning, the learning of mobile robots often requires a simulator and an enormous amount of evolution time so as to perform a task. Therefore, we are to integrate Q-learning into the evolution fuzzy-neural networks to avoid the requirement of the simulator. Experiment results of a mobile robot illustrate the performance of the proposed approach.
UR - http://www.scopus.com/inward/record.url?scp=55249112487&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=55249112487&partnerID=8YFLogxK
U2 - 10.1109/FUZZY.2008.4630629
DO - 10.1109/FUZZY.2008.4630629
M3 - Conference contribution
AN - SCOPUS:55249112487
SN - 9781424418190
SP - 1902
EP - 1906
BT - 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008
ER -