Q-learning optimization in a multi-agents system for image segmentation
This addresses a problem for users in computer vision, but it appears incremental as it applies existing Q-learning to a known multi-agent framework.
The paper tackles the challenge of selecting and ordering operators with appropriate parameters in computer vision by proposing a multi-agent system using the Vowel approach and Q-learning for optimization, with an implementation to test and validate the method.
To know which operators to apply and in which order, as well as attributing good values to their parameters is a challenge for users of computer vision. This paper proposes a solution to this problem as a multi-agent system modeled according to the Vowel approach and using the Q-learning algorithm to optimize its choice. An implementation is given to test and validate this method.