Web: http://arxiv.org/abs/2206.08332

June 17, 2022, 1:12 a.m. | Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pîslar, Bernardo Avila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello

stat.ML updates on arXiv.org arxiv.org

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 …

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