Adversarial policies in Go
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About
FAR.ai's research focuses on adversarial policies against superhuman Go AIs. They discovered simple adversarial strategies that can beat state-of-the-art AIs like KataGo. The research explores a 'cyclic adversary' which beats KataGo more than 97% of the time at superhuman settings. This strategy is replicable by amateur human players and transfers to other superhuman Go AIs. They also explore defenses like positional and iterated adversarial training, and a vision transformer-based Go AI. The results indicate that achieving robustness in Go AIs is challenging, even with defenses, as new adversarial strategies can still exploit vulnerabilities.
Platform
Task
Features
• iterated adversarial training
• positional adversarial training
• defense strategies against adversarial attacks
• vision transformer go ai
• cyclic attack strategy
• adversarial policies for go ais
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