Sample inefficiency refers to the phenomenon where a learning algorithm requires a large number of training samples to achieve a satisfactory level of performance. This issue is particularly relevant in robotic control, where training neural networks often involves significant amounts of data from simulations or real-world interactions, leading to extended training times and resource consumption.
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