Artificial intelligence has achieved remarkable success in recent years, largely driven by advancements in foundation models, which leverage complex neural networks trained on vast amounts of data in order to perform a variety of tasks, such as question answering, text summarization, and image generation. This project seeks to extend the success of foundation models to sequential decision-making, where an agent--a programmable entity---interacts with an environment, seeking to accomplish a task by taking a series of actions over time, with each action influenced by the outcomes of previous actions. Sequential decision-making commonly arises in situations characterized by uncertainty, limited resources, or dynamic conditions, where each decision can have an impact on future actions. The objective is to select a sequence of actions that maximizes profits, rewards, utilities, or some other well-defined objective. Adapting foundation models for sequential decision-making is challenging, because high-quality data is often lacking and it requires recognizing task-specific structures and optimizing long-term objectives, where minor differences can drastically change optimal solutions. This project will develop novel methods for overcoming these challenges to significantly increase the applicability of foundation models for a wide range of sequential decision-making applications, such as smart manufacturing, multi-agent systems, and human-machine interaction. This project will de