We find considerable progress in the sample efficiency of DRL at rates comparable to progress in algorithmic efficiency in deep learning. If the trends we observed proved to be robust and continued, the huge amounts of simulated data that are currently necessary to achieve state-of-the-art results in DRL might not be required for future applications such that training in real world contexts could become feasible.

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