  Training populations of agents has demonstrated great promise in
Reinforcement Learning for stabilizing training, improving exploration and
asymptotic performance, and generating a diverse set of solutions. However,
population-based training is often not considered by practitioners as it is
perceived to be either prohibitively slow (when implemented sequentially), or
computationally expensive (if agents are trained in parallel on independent
accelerators). In this work, we compare implementations and revisit previous
studies to show that the judicious use of compilation and vectorization allows
population-based training to be performed on a single machine with one
accelerator with minimal overhead compared to training a single agent. We also
show that, when provided with a few accelerators, our protocols extend to large
population sizes for applications such as hyperparameter tuning. We hope that
this work and the public release of our code will encourage practitioners to
use population-based learning more frequently for their research and
applications.
