Sample efficiency refers to the ability of a learning algorithm to achieve good performance with fewer training samples. This concept is crucial in various fields, especially when data collection is expensive, time-consuming, or difficult. In reinforcement learning and robot control, sample efficiency helps agents learn optimal policies faster by making the most of each interaction with their environment. In deep learning for perception and decision-making, improving sample efficiency allows models to generalize better from limited data, leading to more effective and reliable systems.
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