The authors consider two-player zero-sum matrix games where the learner observes only the opponent's action and a noisy payoff for the sampled action pair (bandit feedback).
Polylogarithmic Nash regret achieved for any finite matrix game
Optimistic Payoff Balancing extends bandit-feedback guarantees from 2x2 to general dimensions, resolving an open problem.
Academic
Yuheng Zhang
University of Illinois Urbana-Champaign
Research Digest··2 min read
The authors study Nash regret minimization in unknown finite matrix games with bandit payoff feedback and observed opponent actions.
Why this paper
From University of Illinois Urbana-Champaign
In one line
Optimistic Payoff Balancing achieves polylogarithmic Nash regret in arbitrary finite matrix games with bandit feedback.
What we could check
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