  Despite the considerable attention given to the questions of \textit{how
much} and \textit{how to} explore in deep reinforcement learning, the
investigation into \textit{when} to explore remains relatively less researched.
While more sophisticated exploration strategies can excel in specific, often
sparse reward environments, existing simpler approaches, such as
$\epsilon$-greedy, persist in outperforming them across a broader spectrum of
domains. The appeal of these simpler strategies lies in their ease of
implementation and generality across a wide range of domains. The downside is
that these methods are essentially a blind switching mechanism, which
completely disregards the agent's internal state. In this paper, we propose to
leverage the agent's internal state to decide \textit{when} to explore,
addressing the shortcomings of blind switching mechanisms. We present Value
Discrepancy and State Counts through homeostasis (VDSC), a novel approach for
efficient exploration timing. Experimental results on the Atari suite
demonstrate the superiority of our strategy over traditional methods such as
$\epsilon$-greedy and Boltzmann, as well as more sophisticated techniques like
Noisy Nets.
