Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Target Reaching by Using Visual Information and Q-learning Controllers
Autonomous Robots
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Monte Carlo Off-Policy Reinforcement Learning: A Rough Set Approach
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Motion sketch: acquisition of visual motion guided behaviors
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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This paper presents a method of target tracking for a robotic vision system employing reinforcement learning with feedback based on average rough coverage performance values. The application is for a line-crawling inspection robot (ALiCE II, the second revision of Automated Line Crawling Equipment) designed to automate the inspection of hydro electric transmission lines and related equipment. The problem considered in this paper is how to train the vision system to track targets of interest and acquire useful images for further analysis. To train the system, two versions of Watkins' Q-learning were implemented, the classical single-step version and a modified strain using an approximation space-based form of what we term rough feedback. The robot is briefly described along with experimental results for the two forms of the Q-learning control algorithm. The contribution of this article is an introduction to a modified version of Q-learning control with rough feedback to monitor and adjust the learning rate during target tracking.