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- W4285445950 abstract "Reinforcement Learning (RL) is a widely used method for solving optimal problems without system knowledge. However, the use of RL for control of industrial applications is still reduced. One of the reasons for limited applicability of RL in this field is the difficulty of learning the system behaviour under poor experimental conditions. This paper proposes two methods to cope with scenarios where the data collected is not contributing to the learning in linear systems. The first method identifies the periods where the learning is not efficient and pauses the policy update, the second method applies a reduction of the approximation space to continue with the learning. The proposed methods are validated in a simulation environment of a water distribution network. Both methods show similar performance and provide a reliable operation during steady state or poor experimental conditions." @default.
- W4285445950 created "2022-07-15" @default.
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- W4285445950 date "2021-06-29" @default.
- W4285445950 modified "2023-10-18" @default.
- W4285445950 title "Real-Time Reinforcement Learning Control in Poor Experimental Conditions*" @default.
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- W4285445950 doi "https://doi.org/10.23919/ecc54610.2021.9654896" @default.
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