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- W1489237453 abstract "In most existing motion control algorithms, a reference trajectory is tracked, based on a continuous measurement of the system’s response. In many industrial applications, however, it is either not possible or too expensive to install sensors which measure the system’s output over the complete stroke: instead, the motion can only be detected at certain discrete positions. The control objective in these systems is often not to track a complete trajectory accurately, but rather to achieve a given state at the sensor locations (e.g. to pass by the sensor at a given time, or with a given speed). Model-based control strategies are not suited for the control of these systems, due to the lack of sensor data. We are currently investigating the potential of a non-modelbased learning strategy, Reinforcement Learning (RL), in dealing with this kind of discrete sensor information. Here, we describe experiments with a simple yet challenging system, where a single sensor detects the passage of a mass being pushed by a linear motor." @default.
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- W1489237453 date "2011-05-20" @default.
- W1489237453 modified "2023-09-26" @default.
- W1489237453 title "Policy gradient methods for controlling systems with discrete sensor information" @default.
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