Learning Fair Division from Bandit Feedback

no code implementations • 15 Nov 2023 • Hakuei Yamada, Junpei Komiyama, Kenshi Abe, Atsushi Iwasaki

This work addresses learning online fair division under uncertainty, where a central planner sequentially allocates items without precise knowledge of agents' values or utilities.

Adaptively Perturbed Mirror Descent for Learning in Games

1 code implementation • 26 May 2023 • Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Atsushi Iwasaki

This paper proposes a payoff perturbation technique for the Mirror Descent (MD) algorithm in games where the gradient of the payoff functions is monotone in the strategy profile space, potentially containing additive noise.

Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games

1 code implementation • 21 Aug 2022 • Kenshi Abe, Kaito Ariu, Mitsuki Sakamoto, Kentaro Toyoshima, Atsushi Iwasaki

This paper proposes Mutation-Driven Multiplicative Weights Update (M2WU) for learning an equilibrium in two-player zero-sum normal-form games and proves that it exhibits the last-iterate convergence property in both full and noisy feedback settings.

Multi-agent Reinforcement Learning

Mutation-Driven Follow the Regularized Leader for Last-Iterate Convergence in Zero-Sum Games

1 code implementation • 18 Jun 2022 • Kenshi Abe, Mitsuki Sakamoto, Atsushi Iwasaki

In this study, we consider a variant of the Follow the Regularized Leader (FTRL) dynamics in two-player zero-sum games.

Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search

1 code implementation • 14 Feb 2022 • Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki

Constructing a good search tree representation significantly boosts the performance of the proposed method.