Atsushi IWASAKI (岩﨑敦)

0110100_Ⅰ類（情報系）, 0110000_情報理工学域, The University of Electro-Communications

Game theory | ゲーム理論

Market Design | マーケットデザイン

Optimization | 最適化

Algorithm | アルゴリズム

Learning with Imperfect Information | 不完全情報下の学習

ジャーナル論文 - rm_published_papers: Scientific Journal 査読済み

Evaluating the Efficiency of Regulation in Matching Markets with Distributional Disparities

by Atsushi IWASAKI

公開済 02/07/2025

Proceedings of the 26th ACM Conference on Economics and Computation, 94 - 94

ジャーナル論文 - rm_published_papers: Scientific Journal 査読済み

Approximate State Abstraction for Markov Games

by Atsushi IWASAKI

公開済 11/04/2025

Proceedings of the AAAI Conference on Artificial Intelligence, 39, 17, 17555 - 17563

This paper introduces state abstraction for two-player zero-sum Markov games (TZMGs) where the payoffs for the two players are determined by the state representing the environment and their respective actions, with state transitions following a Markov decision processes. For example, in games like soccer, the value of actions changes according to the state of play, we should describe them as Markov games. In TZMGs, the more the number of states becomes, the more difficult computing the equilibrium becomes. Therefore, we abstract the states of TZMGs and examine the performance. State abstraction reduces the number of states by treating multiple different states as a single state, and there is a substantial body of research on finding optimal policies for Markov decision processes using state abstraction. This study extends the state abstraction for MDPs to Markov games. In this case, the game with state abstraction may yield different equilibrium solutions from those of the ground game. To evaluate the equilibrium solutions of the game with state abstraction, we derived bounds on duality gap, which represents the distance from the equilibrium solutions of the ground game. Finally, we demonstrate our state abstraction with Markov Soccer, compute equilibrium policies, and examine the results.

ジャーナル論文 - rm_published_papers: Scientific Journal 査読済み

Boosting Perturbed Gradient Ascent for Last-Iterate Convergence in Games,

by Atsushi IWASAKI

公開済 04/2025

The Thirteenth International Conference on Learning Representations