  Exploration remains a central challenge for reinforcement learning (RL).
Virtually all existing methods share the feature of a monolithic behaviour
policy that changes only gradually (at best). In contrast, the exploratory
behaviours of animals and humans exhibit a rich diversity, namely including
forms of switching between modes. This paper presents an initial study of
mode-switching, non-monolithic exploration for RL. We investigate different
modes to switch between, at what timescales it makes sense to switch, and what
signals make for good switching triggers. We also propose practical algorithmic
components that make the switching mechanism adaptive and robust, which enables
flexibility without an accompanying hyper-parameter-tuning burden. Finally, we
report a promising and detailed analysis on Atari, using two-mode exploration
and switching at sub-episodic time-scales.
