Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6874584 | Journal of Computational Science | 2015 | 39 Pages |
Abstract
In this paper, a novel reinforcement learning method inspired by the way humans learn from others is presented. This method is developed based on cellular learning automata featuring a modular design and cooperation techniques. The modular design brings flexibility, reusability and applicability in a wide range of problems to the method. This paper focuses on analyzing sensitivity of the method's parameters and the applicability in optimization problems. Results of the experiments justify that the new method outperforms similar ones because of employing knowledge sharing technique, reasonable exploration logic, and learning rules based on the action trajectory.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Milad Mozafari, Mohammad Ebrahim Shiri, Hamid Beigy,