  Supporting state-of-the-art AI research requires balancing rapid prototyping,
ease of use, and quick iteration, with the ability to deploy experiments at a
scale traditionally associated with production systems.Deep learning frameworks
such as TensorFlow, PyTorch and JAX allow users to transparently make use of
accelerators, such as TPUs and GPUs, to offload the more computationally
intensive parts of training and inference in modern deep learning systems.
Popular training pipelines that use these frameworks for deep learning
typically focus on (un-)supervised learning. How to best train reinforcement
learning (RL) agents at scale is still an active research area. In this report
we argue that TPUs are particularly well suited for training RL agents in a
scalable, efficient and reproducible way. Specifically we describe two
architectures designed to make the best use of the resources available on a TPU
Pod (a special configuration in a Google data center that features multiple TPU
devices connected to each other by extremely low latency communication
channels).
