  Animals have a developed ability to explore that aids them in important tasks
such as locating food, exploring for shelter, and finding misplaced items.
These exploration skills necessarily track where they have been so that they
can plan for finding items with relative efficiency. Contemporary exploration
algorithms often learn a less efficient exploration strategy because they
either condition only on the current state or simply rely on making random
open-loop exploratory moves. In this work, we propose $\eta\psi$-Learning, a
method to learn efficient exploratory policies by conditioning on past episodic
experience to make the next exploratory move. Specifically, $\eta\psi$-Learning
learns an exploration policy that maximizes the entropy of the state visitation
distribution of a single trajectory. Furthermore, we demonstrate how variants
of the predecessor representation and successor representations can be combined
to predict the state visitation entropy. Our experiments demonstrate the
efficacy of $\eta\psi$-Learning to strategically explore the environment and
maximize the state coverage with limited samples.
