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learning-in-games

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F10G0 / swap-regret-thesis

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Implementation and empirical evaluation of external, internal, and swap-regret algorithms under full-information and bandit feedback, with applications to repeated games and correlated equilibria.

python game-theory bachelor-thesis multi-armed-bandits online-learning regret-minimization multiplicative-weights repeated-games correlated-equilibrium learning-in-games empirical-evaluation no-regret-learning adversarial-bandits coarse-correlated-equilibrium external-regret internal-regret swap-regret
  • Updated Sep 30, 2026
  • Python

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