What is a non-starving policy in reinforcement learning? In the paper, Eligibility Traces for off-Policy Policy Evaluation (2010), by Doina Precup et al., mentioned the term "non-starving" many times. The specific use of the term was like "non-starving policy" in the context of off-policy learning. 

A specific mention of the term


  we consider a method that requires nothing of the behavior
  policy other than that it be non-starving, i.e., that it
  never reaches a time when some state-action pair is never
  visited again.


What does the thing look like intuitively? Why is it required?
A non-starving policy is a (behavior) policy that is theoretically guaranteed to visit each state and take all possible actions from each state an infinite number of times, so that to always update $Q(s, a)$, $\forall s, \forall a$, an infinite number of times. In the context of off-policy prediction, this criterion implies that any trajectory will have no zero probability under a behavior policy. As a consequence, the experience from the behavior policy sufficiently covers the possibilities of any target policy.

An example of a non-starving policy is the $\epsilon$-greedy policy, which, with $0 &lt; \epsilon \leq 1$ (which is usually a small number between $0$ and $1$) probability, takes a random action from a given state, and, with $1-\epsilon$ probability, takes the current best action, that is, the action with the highest value from a given state, according to the current value function.