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.

We assess AOC's performance in both simulated and real-world healthcare scenarios, emphasizing its capability to manage offline control tasks with high

This paper considers the problem of designing optimal algorithms for reinforcement learning in two-player zero-sum games. We focus on self-play algorithms which learn theoptimal policy by playing againstitself without any direct supervision.