Searched for: subject%3A%22Distributional%255C+Reinforcement%255C+Learning%22
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Homola, Marek (author)
In the rapidly evolving aviation sector, the quest for safer and more efficient flight operations has historically relied on traditional Automatic Flight Control Systems (AFCS) based on high-fidelity models. However, such models not only incur high development costs but also struggle to adapt to new, complex aircraft designs and unexpected...
master thesis 2024
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Seres, Peter (author)
With the recent increase in the complexity of aerospace systems and autonomous operations, there is a need for an increased level of adaptability and model-free controller synthesis. Such operations require the controller to maintain safety and performance without human intervention in non-static environments with partial observability and...
master thesis 2022
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Dai, Pengcheng (author), Yu, Wenwu (author), Wen, Guanghui (author), Baldi, S. (author)
In this article, the dynamic economic dispatch (DED) problem for smart grid is solved under the assumption that no knowledge of the mathematical formulation of the actual generation cost functions is available. The objective of the DED problem is to find the optimal power output of each unit at each time so as to minimize the total generation...
journal article 2020
document
Greevink, Thijs (author)
This thesis tests the hypothesis that distributional deep reinforcement learning (RL) algorithms get an increased performance over expectation based deep RL because of the regularizing effect of fitting a more complex model. This hypothesis was tested by comparing two variations of the distributional QR-DQN algorithm combined with prioritized...
master thesis 2019
Searched for: subject%3A%22Distributional%255C+Reinforcement%255C+Learning%22
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