By Topic

Evolutionary learning, reinforcement learning, and fuzzy rules for knowledge acquisition in agent-based systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
A. Bonarini ; Dept. of Electron. & Inf., Politecnico di Milano, Italy

The behavior of agents in complex and dynamic environments cannot be programmed a priori, but needs to self-adapt to the specific situations. We present some approaches based on evolutionary reinforcement learning algorithms, which are able to evolve in real-time fuzzy models that control behaviors. We discuss an application where an agent learns how to adapt its behavior to the different behaviors of the other agents it is interacting with, and another application where a group of agents co-evolve cooperative behaviors by using explicit communication to propose the cooperation and to distribute reinforcement to the others

Published in:

Proceedings of the IEEE  (Volume:89 ,  Issue: 9 )