Approximate exploitability: Learning a best response in large games. (arXiv:2004.09677v4 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2004.09677

Researchers have demonstrated that neural networks are vulnerable to
adversarial examples and subtle environment changes, both of which one can view
as a form of distribution shift. To humans, the resulting errors can look like
blunders, eroding trust in these agents. In prior games research, agent
evaluation often focused on the in-practice game outcomes. While valuable, such
evaluation typically fails to evaluate robustness to worst-case outcomes. Prior
research in computer poker has examined how to assess such worst-case
performance, both …

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