2023
DOI: 10.3389/frai.2023.1098982
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How to train a self-driving vehicle: On the added value (or lack thereof) of curriculum learning and replay buffers

Abstract: Learning from only real-world collected data can be unrealistic and time consuming in many scenario. One alternative is to use synthetic data as learning environments to learn rare situations and replay buffers to speed up the learning. In this work, we examine the hypothesis of how the creation of the environment affects the training of reinforcement learning agent through auto-generated environment mechanisms. We take the autonomous vehicle as an application. We compare the effect of two approaches to genera… Show more

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Cited by 1 publication
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References 38 publications
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