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.

翻译：真实世界环境中普遍存在的不确定性和动态性，给机器驱动的智能决策系统的广泛应用带来了重大挑战。因此，智能决策应具备持续获取新技能并有效泛化至广泛领域的能力。超越任务与应用边界的人工通用智能的进步，对于提升智能决策至关重要。近期研究广泛探索了Transformer神经架构作为计算机视觉、自然语言处理及强化学习等各类任务的基础模型。我们提出，通过将多样化决策任务形式化为基于Transformer架构的序列解码任务，可构建基础决策模型，这为扩展智能决策在复杂真实场景中的应用提供了有前景的解决方案。本文讨论了基础决策模型为智能决策带来的效率与泛化改进，并探索了其在多智能体游戏AI、生产调度及机器人任务中的潜在应用。最后，我们通过案例研究展示了所实现的基础决策模型——包含13亿参数的DigitalBrain（DB1），该模型在870项任务中达到人类水平性能，涵盖文本生成、图像描述、视频游戏操控、机器人控制及旅行商问题等。作为基础决策模型，DB1标志着向更自主、高效的真实世界智能决策应用迈出了初步一步。