Abstract. Q-learning can be used to learn a control policy that maximises a scalar reward through interaction with the environment. Qlearning is commonly applied to problems with discrete states and actions. We describe a method suitable for control tasks which require continuous actions, in response to continuous states. The system consists of a neural network coupled with a novel interpolator. Simulation results are presented for a non-holonomic control task. Advantage Learning, a variation of Q-learning, is shown enhance learning speed and reliability for this task.
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.
In this paper we discuss active humanoid vision systems that realize foveation using two rigidly connected cameras in each eye. We present an exhaustive analysis of the relationship between the positions of the observed point in the foveal and peripheral view with respect to the intrinsic and extrinsic parameters of both cameras and 3-D point position. Based on these results we propose a control scheme that can be used to maintain the view of the observed object in the foveal image using information from the peripheral view. Experimental results showing the effectiveness of the proposed foveation control are also provided.
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