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.

BYOL-Explore 通过优化单一预测损失，在潜在空间中学习世界表示、世界动态和探索策略，实现在视觉复杂环境下的好奇心驱动探索。此方法可有效解决部分可观察连续行动的具有挑战性的难度探索基准和 Atari 游戏等任务。