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Computer Science > Machine Learning

arXiv:2211.00241 (cs)
[Submitted on 1 Nov 2022 (v1), last revised 13 Jul 2023 (this
version, v4)]

Title:Adversarial Policies Beat Superhuman Go AIs

Authors:Tony T. Wang, Adam Gleave, Tom Tseng, Kellin Pelrine, Nora
Belrose, Joseph Miller, Michael D. Dennis, Yawen Duan, Viktor
Pogrebniak, Sergey Levine, Stuart Russell
View a PDF of the paper titled Adversarial Policies Beat Superhuman
Go AIs, by Tony T. Wang and 10 other authors
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    Abstract:We attack the state-of-the-art Go-playing AI system
    KataGo by training adversarial policies against it, achieving a >
    97% win rate against KataGo running at superhuman settings. Our
    adversaries do not win by playing Go well. Instead, they trick
    KataGo into making serious blunders. Our attack transfers
    zero-shot to other superhuman Go-playing AIs, and is
    comprehensible to the extent that human experts can implement it
    without algorithmic assistance to consistently beat superhuman
    AIs. The core vulnerability uncovered by our attack persists even
    in KataGo agents adversarially trained to defend against our
    attack. Our results demonstrate that even superhuman AI systems
    may harbor surprising failure modes. Example games are available
    this https URL.

Comments:    Accepted to ICML 2023, see paper for changelog
             Machine Learning (cs.LG); Artificial Intelligence
Subjects:    (cs.AI); Cryptography and Security (cs.CR); Machine
             Learning (stat.ML)
ACM classes: I.2.6
Cite as:     arXiv:2211.00241 [cs.LG]
             (or arXiv:2211.00241v4 [cs.LG] for this version)
             https://doi.org/10.48550/arXiv.2211.00241
             Focus to learn more
             arXiv-issued DOI via DataCite

Submission history

From: Tony Wang [view email]
[v1] Tue, 1 Nov 2022 03:13:20 UTC (838 KB)
[v2] Mon, 9 Jan 2023 19:53:05 UTC (6,054 KB)
[v3] Sat, 18 Feb 2023 22:05:01 UTC (6,849 KB)
[v4] Thu, 13 Jul 2023 06:37:29 UTC (4,698 KB)
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