This paper introduces deep synoptic Monte Carlo planning (DSMCP) for large imperfect information games.

The concept of information structure is also fundamental to studying the phenomenon ofpartial observability.

GATAis trained using acombination of reinforcement and self-supervised learning. Our workdemonstrates thatthelearned graph-based representations helpagents converge to better policies than their text-only counterparts and facilitate effective generalization across game configurations.