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New paper explores regret, equilibrium, and learning in games

By PulseAugur Editorial · [1 sources] ·

A new paper provides a comprehensive overview of learning in games, exploring both single-agent decision processes and multi-agent interactions. It introduces a family of regularized learning policies designed to balance exploration with exploitation. The work presents regret bounds for adversarial bandits and an equilibrium convergence result for zero-sum games, linking strategic stability with dynamic learning attractors. AI

RANK_REASON The item is a research paper submitted to arXiv. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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

New paper explores regret, equilibrium, and learning in games

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The item is a research paper submitted to arXiv. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Panayotis Mertikopoulos ·

    Regret, equilibrium, and learning in games: A guided tour

    arXiv:2608.09389v1 Announce Type: cross Abstract: This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learning and data science to economics and beyond. Our …

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