UC Santa Barbara

In recent years, there has been an increasing emphasis on developing generalist agents capable of solving a diverse variety of tasks effectively. We hope that such an agent would be capable of chaining several smaller tasks together, navigating from high-dimensional inputs, and being simple and reliable to train. In this thesis, we will first introduce the current landscape of generalist agents and the state of Deep Reinforcement Learning (RL) and Offline RL. We'll also discuss several major issues underlying these fields, such as training instability and generalization failure. Next, we will explore several proposals for solving such problems, namely through optimization techniques and diffusion. Finally, we will discuss the major challenges and opportunities that lie ahead in the future for training generalist agents.