Generative Decision Making Under Uncertainty

The ability to make sequential decisions under uncertainty is a key component of intelligence. Despite impressive breakthroughs in deep learning in the last decade, we find that scalable and generalizable decision making has so far been elusive for current artificial intelligence (AI) systems. In this talk, I will present a new framework for sequential decision making that is derived from modern generative models for language and perception. We will instantiate our framework in 3 different paradigms for sequential decision making: offline reinforcement learning (RL), online RL, and black-box optimization, and highlight the simplicity and effectiveness of this unifying framework on a range of challenging high-dimensional benchmarks for sequential decision making.