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New learning dynamics achieve constant swap regret in general-sum games

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new deterministic and uncoupled learning dynamics for multiplayer general-sum games that achieve constant individual swap regret. This means each player's regret is independent of the game's duration. The proposed method involves players predicting deviation gains, updating a transition matrix, and playing a stationary distribution. An adversarial variant of this approach also guarantees a bounded individual swap regret in non-cooperative settings. AI

IMPACT Introduces novel theoretical frameworks for strategic decision-making in multi-agent systems, potentially impacting AI research in game theory and reinforcement learning.

RANK_REASON The cluster contains an academic paper detailing new theoretical findings in game theory and learning dynamics.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New learning dynamics achieve constant swap regret in general-sum games

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Tung Mai ·

    Constant Swap Regret in General-Sum Games via Optimistic Transition Matrices

    arXiv:2609.16751v1 Announce Type: cross Abstract: We give deterministic and uncoupled learning dynamics for finite multiplayer general-sum games under full-information feedback that achieve constant individual swap regret, independent of the horizon $T$. With $n$ players and at m…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Constant Swap Regret in General-Sum Games via Optimistic Transition Matrices

    We give deterministic and uncoupled learning dynamics for finite multiplayer general-sum games under full-information feedback that achieve constant individual swap regret, independent of the horizon $T$. With $n$ players and at most $m$ actions each, the individual swap regret o…

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