  We present BYOL-Explore, a conceptually simple yet general approach for
curiosity-driven exploration in visually-complex environments. BYOL-Explore
learns a world representation, the world dynamics, and an exploration policy
all-together by optimizing a single prediction loss in the latent space with no
additional auxiliary objective. We show that BYOL-Explore is effective in
DM-HARD-8, a challenging partially-observable continuous-action
hard-exploration benchmark with visually-rich 3-D environments. On this
benchmark, we solve the majority of the tasks purely through augmenting the
extrinsic reward with BYOL-Explore s intrinsic reward, whereas prior work could
only get off the ground with human demonstrations. As further evidence of the
generality of BYOL-Explore, we show that it achieves superhuman performance on
the ten hardest exploration games in Atari while having a much simpler design
than other competitive agents.
