  Recent advances in machine learning are consistently enabled by increasing
amounts of computation. Reinforcement learning (RL) and population-based
methods in particular pose unique challenges for efficiency and flexibility to
the underlying distributed computing frameworks. These challenges include
frequent interaction with simulations, the need for dynamic scaling, and the
need for a user interface with low adoption cost and consistency across
different backends. In this paper we address these challenges while still
retaining development efficiency and flexibility for both research and
practical applications by introducing Fiber, a scalable distributed computing
framework for RL and population-based methods. Fiber aims to significantly
expand the accessibility of large-scale parallel computation to users of
otherwise complicated RL and population-based approaches without the need to
for specialized computational expertise.
