  A central component of training in Reinforcement Learning (RL) is Experience:
the data used for training. The mechanisms used to generate and consume this
data have an important effect on the performance of RL algorithms.
  In this paper, we introduce Reverb: an efficient, extensible, and easy to use
system designed specifically for experience replay in RL. Reverb is designed to
work efficiently in distributed configurations with up to thousands of
concurrent clients.
  The flexible API provides users with the tools to easily and accurately
configure the replay buffer. It includes strategies for selecting and removing
elements from the buffer, as well as options for controlling the ratio between
sampled and inserted elements. This paper presents the core design of Reverb,
gives examples of how it can be applied, and provides empirical results of
Reverb's performance characteristics.
