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Reinforcement learning for a vision based mobile robot

Gaskett, C; Fletcher, Luke; Zelinsky, Alex


Reinforcement learning systems improve behaviour based on scalar rewards from a critic. In this work vision based behaviours, servoing and wandering, are learned through a Q-learning method which handles continuous states and actions. There is no requirement for camera calibration, an actuator model, or a knowledgeable teacher. Learning through observing the actions of other behaviours improves learning speed. Experiments were performed on a mobile robot using a real-time vision system.

CollectionsANU Research Publications
Date published: 2000
Type: Conference paper
Source: Proeedings of IROS-2000


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