  Most exploration research on reinforcement learning (RL) has paid attention
to `the way of exploration', which is `how to explore'. The other exploration
research, `when to explore', has not been the main focus of RL exploration
research. The issue of `when' of a monolithic exploration in the usual RL
exploration behaviour binds an exploratory action to an exploitational action
of an agent. Recently, a non-monolithic exploration research has emerged to
examine the mode-switching exploration behaviour of humans and animals. The
ultimate purpose of our research is to enable an agent to decide when to
explore or exploit autonomously. We describe the initial research of an
autonomous multi-mode exploration of non-monolithic behaviour in an options
framework. The higher performance of our method is shown against the existing
non-monolithic exploration method through comparative experimental results.
