  One of the biggest challenges to modern deep reinforcement learning (DRL)
algorithms is sample efficiency. Many approaches learn a world model in order
to train an agent entirely in imagination, eliminating the need for direct
environment interaction during training. However, these methods often suffer
from either a lack of imagination accuracy, exploration capabilities, or
runtime efficiency. We propose Hieros, a hierarchical policy that learns time
abstracted world representations and imagines trajectories at multiple time
scales in latent space. Hieros uses an S5 layer-based world model, which
predicts next world states in parallel during training and iteratively during
environment interaction. Due to the special properties of S5 layers, our method
can train in parallel and predict next world states iteratively during
imagination. This allows for more efficient training than RNN-based world
models and more efficient imagination than Transformer-based world models.
  We show that our approach outperforms the state of the art in terms of mean
and median normalized human score on the Atari 100k benchmark, and that our
proposed world model is able to predict complex dynamics very accurately. We
also show that Hieros displays superior exploration capabilities compared to
existing approaches.
