  Sampled environment transitions are a critical input to deep reinforcement
learning (DRL) algorithms. Current DRL benchmarks often allow for the cheap and
easy generation of large amounts of samples such that perceived progress in DRL
does not necessarily correspond to improved sample efficiency. As simulating
real world processes is often prohibitively hard and collecting real world
experience is costly, sample efficiency is an important indicator for
economically relevant applications of DRL. We investigate progress in sample
efficiency on Atari games and continuous control tasks by comparing the number
of samples that a variety of algorithms need to reach a given performance level
according to training curves in the corresponding publications. We find
exponential progress in sample efficiency with estimated doubling times of
around 10 to 18 months on Atari, 5 to 24 months on state-based continuous
control and of around 4 to 9 months on pixel-based continuous control depending
on the specific task and performance level.
