BYOL-Explore: Exploration by Bootstrapped Prediction

Zhaohan Guo · Shantanu Thakoor · Miruna Pislar · Bernardo Avila Pires · Florent Altché · Corentin Tallec · Alaa Saade · Daniele Calandriello · Jean-Bastien Grill · Yunhao Tang · Michal Valko · Remi Munos · Mohammad Gheshlaghi Azar · Bilal Piot

Hall J #911

Keywords: [ Deep Reinforcement Learning ] [ Exploration ] [ Representation Learning ] [ Self-supervised learning ]

[ Abstract ]
[ Paper [ Poster [ OpenReview
Thu 1 Dec 2 p.m. PST — 4 p.m. PST


We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually complex environments. BYOL-Explore learns the world representation, the world dynamics and the 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 environment. On this benchmark, we solve the majority of the tasks purely through augmenting the extrinsic reward with BYOL-Explore 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.

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