In Sutton & Barto Reinforcement learning book, page 103 (chapter: off-policy learning via importance sampling), the following statement is given:
"In order to use episodes from $b$ to estimate values for $\pi$, we require that every action taken under $\pi$ is also taken, at least occasionally, under $b$. That is, we require that $\pi$(a|s) > 0 implies $b$(a|s) > 0. This is called the assumption of coverage. It follows from coverage that $b$ must be stochastic in states where it is not identical to $\pi$. "
where $b$ refers to a behavior policy and $\pi$ to the target optimal policy. I don't understand how the above conclusion was obtained from the definition of coverage.