Sample efficiency

Topic

Sample efficiency refers to how effectively a machine learning algorithm, particularly in reinforcement learning, utilizes training data or environmental interactions to learn a target function or optimal policy. An algorithm is considered sample efficient if it requires a relatively small number of samples or interactions to achieve a high level of performance. It is closely related to the theoretical concept of sample complexity, which defines the mathematical bounds on the number of training samples needed for successful learning.