Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Parameter Optimisation of an Image Processing System Using Evolutionary Algorithms
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
A Reinforcement Learning Framework for Parameter Control in Computer Vision Applications
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
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Selecting the appropriate operators with the optimal values for their parameters represents a big challenge for users. In this paper we present a solution for this problem. This solution uses a multi-agent architecture based on reinforcement learning to automate the process of operator selection and parameter adjustment. The architecture consists of three types of agents: the User Agent, the Operator Agent and the Parameter Agent. The User Agent determines the phases of treatment, and for each phase it determines a library of possible operators and possible values of their parameters. The Operator Agent constructs all possible combinations of operators and decides for the best one. The Parameter Agent, the core of the architecture, adjusts the parameters of each combination of operators by processing a large number of images. Towards the end, the agents must offer the best combination of operators and the best values of their parameters.