  Very large state spaces with a sparse reward signal are difficult to explore.
The lack of a sophisticated guidance results in a poor performance for numerous
reinforcement learning algorithms. In these cases, the commonly used random
exploration is often not helpful. The literature shows that this kind of
environments require enormous efforts to systematically explore large chunks of
the state space. Learned state representations can help here to improve the
search by providing semantic context and build a structure on top of the raw
observations. In this work we introduce a novel time-myopic state
representation that clusters temporal close states together while providing a
time prediction capability between them. By adapting this model to the
Go-Explore paradigm (Ecoffet et al., 2021b), we demonstrate the first learned
state representation that reliably estimates novelty instead of using the
hand-crafted representation heuristic. Our method shows an improved solution
for the detachment problem which still remains an issue at the Go-Explore
Exploration Phase. We provide evidence that our proposed method covers the
entire state space with respect to all possible time trajectories without
causing disadvantageous conflict-overlaps in the cell archive. Analogous to
native Go-Explore, our approach is evaluated on the hard exploration
environments MontezumaRevenge, Gravitar and Frostbite (Atari) in order to
validate its capabilities on difficult tasks. Our experiments show that
time-myopic Go-Explore is an effective alternative for the domain-engineered
heuristic while also being more general. The source code of the method is
available on GitHub.
