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Part of what makes Deep Learning work is having a lot of data about your problem, whether that be labelled data, points from a data distribution that you are learning to generate, or in the case of reinforcement learning, training episodes. Having lots of data isn’t something that you should always take for granted. Sample efficiency is all about getting more out of the data that you already have. One way that we can get better sample efficiency is by re-framing problems that have quite complex data into more abstract problems which have simpler data. If you can generalize problems in this way, you can learn how to solve them with much less data.
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