Deep Learning to Play Games

• Tác giả: Daniele Condorelli, Massimiliano Furlan
• Ngôn ngữ: eng
• Ký hiệu phân loại: 006.31 Machine learning
• Thông tin xuất bản: 2024
• Bộ sưu tập: Metadata
• ID: 204263

Tóm tắt

We train two neural networks adversarially to play normal-form games. At each iteration, a row and column network take a new randomly generated game and output individual mixed strategies. The parameters of each network are independently updated via stochastic gradient descent to minimize expected regret given the opponent's strategy. Our simulations demonstrate that the joint behavior of the networks converges to strategies close to Nash equilibria in almost all games. For all $2 \times 2$ and in 80% of $3 \times 3$ games with multiple equilibria, the networks select the risk-dominant equilibrium. Our results show how Nash equilibrium emerges from learning across heterogeneous games.