Article ID Journal Published Year Pages File Type
6874584 Journal of Computational Science 2015 39 Pages PDF
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
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