  We identify and study the phenomenon of policy churn, that is, the rapid
change of the greedy policy in value-based reinforcement learning. Policy churn
operates at a surprisingly rapid pace, changing the greedy action in a large
fraction of states within a handful of learning updates (in a typical deep RL
set-up such as DQN on Atari). We characterise the phenomenon empirically,
verifying that it is not limited to specific algorithm or environment
properties. A number of ablations help whittle down the plausible explanations
on why churn occurs to just a handful, all related to deep learning. Finally,
we hypothesise that policy churn is a beneficial but overlooked form of
implicit exploration that casts $\epsilon$-greedy exploration in a fresh light,
namely that $\epsilon$-noise plays a much smaller role than expected.
