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arXiv · 2206.08332

BYOL-Explore: Exploration by Bootstrapped Prediction

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

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.

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Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pîslar, Bernardo Avila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot. 2022-06-16. BYOL-Explore: Exploration by Bootstrapped Prediction. https://arxiv.org/abs/2206.08332

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