COMPACT FUZZY Q LEARNING FOR AUTONOMOUS MOBILE ROBOT NAVIGATION
Date
2017-10-11Author
Wicaksono, Handy
Anam, Khairul
Prihastono, Prihastono
Sulistijono, Indra Adjie
Kuswadi, Son
Metadata
Show full item recordAbstract
Robot which does complex task needs learning capability.
Q learning is popular reinforcement learning method
because it has off-line policy characteristic and simple
algorithm. But it only suitable in discrete state and action.
By using Fuzzy Q Learning (FQL), continuous state and
action can be handled too. Unfortunately, it’s not easy to
implement FQL algorithm to real robot because its
complexity and robot’s limited memory capacity. In this
research, Compact FQL (CFQL) algorithm is proposed to
solve those weaknesses. By using CFQL, robot still can
accomplish its task in autonomous navigation although its
performance is not as good as robot using FQL.
Collections
- LSP-Conference Proceeding [1874]