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Paper analyzes strategic surplus against FTRL learners in games

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This paper investigates the strategic surplus achievable against a Follow-the-Regularized-Leader (FTRL) learning algorithm in two-player zero-sum games. The research demonstrates that the extraction of this surplus is an inherent characteristic of the FTRL family, scaling with the number of suboptimal actions taken by the learner. The analysis reveals a dichotomy based on regularizer steepness, where non-steep regularizers allow for maximal transient surplus through finite-time elimination of suboptimal actions, while steep regularizers introduce a delay in surplus saturation. AI

IMPACT Provides theoretical insights into the behavior of learning algorithms in game theory, potentially influencing the design of future AI agents.

RANK_REASON This is a research paper published on arXiv detailing theoretical analysis of game theory and learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Paper analyzes strategic surplus against FTRL learners in games

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This is a research paper published on arXiv detailing theoretical analysis of game theory and learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yiheng Su, Emmanouil-Vasileios Vlatakis-Gkaragkounis ·

    No Coin Left Behind: Maximizing Strategic Surplus Against No-Regret Dynamics

    arXiv:2604.05129v2 Announce Type: replace-cross Abstract: We investigate the strategic surplus obtainable against a Follow-the-Regularized-Leader (FTRL) learner with constant step size $\eta$ in $n\times m$ two-player zero-sum games played over $T$ rounds against a clairvoyant op…

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