  Low-precision training has become a popular approach to reduce compute
requirements, memory footprint, and energy consumption in supervised learning.
In contrast, this promising approach has not yet enjoyed similarly widespread
adoption within the reinforcement learning (RL) community, partly because RL
agents can be notoriously hard to train even in full precision. In this paper
we consider continuous control with the state-of-the-art SAC agent and
demonstrate that a na\"ive adaptation of low-precision methods from supervised
learning fails. We propose a set of six modifications, all straightforward to
implement, that leaves the underlying agent and its hyperparameters unchanged
but improves the numerical stability dramatically. The resulting modified SAC
agent has lower memory and compute requirements while matching full-precision
rewards, demonstrating that low-precision training can substantially accelerate
state-of-the-art RL without parameter tuning.
