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 | 2008/06/01 → 2008/06/06 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Software
- Artificial Intelligence
- Applied Mathematics