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Planning Against Learning in Rank-1 Games
Overman 2026-08-18
William Overman
Learning algorithms are often used to make decisions in repeated multi-agent environments. When another player understands how a learner adapts from past experience, that player can plan strategically across rounds to influence the learner's future behavior. Recent work shows that optimizing against Replicator Dynamics, the continuous-time analogue of Multiplicative Weights Update, is tractable in
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Key Contributions
- Learning algorithms are often used to make decisions in repeated multi-agent environments.
- When another player understands how a learner adapts from past experience, that player can plan strategically across rounds to influence the learner's future behavior.
- Recent work shows that optimizing against Replicator Dynamics, the continuous-time analogue of Multiplicative Weights Update, is tractable in
Research Themes
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