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New research explores learning vs. optimizing in budgeted games

By PulseAugur Editorial · [1 sources] ·

A new research paper explores repeated general games with budget constraints, focusing on the interaction between a utility-maximizing optimizer and a learning agent. The study introduces novel calibration methods to ensure non-manipulability and low stochastic regret for the learner, even when budgets complicate decision-making across rounds. The proposed learning algorithm achieves these goals by best responding to calibrated forecasts of the optimizer's play, constructed through simulations of online gradient ascent. AI

IMPACT This research could inform the development of more robust AI agents capable of strategic interaction in complex, resource-constrained environments.

RANK_REASON The item is a research paper published on arXiv detailing theoretical work in game theory. [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 research explores learning vs. optimizing in budgeted games

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The item is a research paper published on arXiv detailing theoretical work in game theory. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Giannis Fikioris, Balasubramanian Sivan, \'Eva Tardos ·

    Learning vs. Optimizing Players in Budgeted Games

    arXiv:2611.05580v1 Announce Type: cross Abstract: We study repeated general games over $T$ rounds between a utility-maximizing optimizer and a learning agent, where budget constraints couple the players' decisions across rounds. Without budgets, low swap regret simultaneously gua…

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