  The pervasive uncertainty and dynamic nature of real-world environments
present significant challenges for the widespread implementation of
machine-driven Intelligent Decision-Making (IDM) systems. Consequently, IDM
should possess the ability to continuously acquire new skills and effectively
generalize across a broad range of applications. The advancement of Artificial
General Intelligence (AGI) that transcends task and application boundaries is
critical for enhancing IDM. Recent studies have extensively investigated the
Transformer neural architecture as a foundational model for various tasks,
including computer vision, natural language processing, and reinforcement
learning. We propose that a Foundation Decision Model (FDM) can be developed by
formulating diverse decision-making tasks as sequence decoding tasks using the
Transformer architecture, offering a promising solution for expanding IDM
applications in complex real-world situations. In this paper, we discuss the
efficiency and generalization improvements offered by a foundation decision
model for IDM and explore its potential applications in multi-agent game AI,
production scheduling, and robotics tasks. Lastly, we present a case study
demonstrating our FDM implementation, DigitalBrain (DB1) with 1.3 billion
parameters, achieving human-level performance in 870 tasks, such as text
generation, image captioning, video game playing, robotic control, and
traveling salesman problems. As a foundation decision model, DB1 represents an
initial step toward more autonomous and efficient real-world IDM applications.
