  In this paper, a unified framework for exploration in reinforcement learning
(RL) is proposed based on an option-critic model. The proposed framework learns
to integrate a set of diverse exploration strategies so that the agent can
adaptively select the most effective exploration strategy over time to realize
a relevant exploration-exploitation trade-off for each given task. The
effectiveness of the proposed exploration framework is demonstrated by various
experiments in the MiniGrid and Atari environments.
