
Explore generative sequence models for decision-making under uncertainty. Learn how Transformer architecture and language modeling advances can enhance offline learning, online exploration, and multi-task generalization in AI.

Discover how artificial agents can learn and maintain libraries of reusable behaviors for solving sequential decision-making problems through programming and neural network approaches.

Delve into sequential decision-making under uncertainty, exploring persuasion and mechanism design in information asymmetry scenarios, with applications to AI and dynamic modeling frameworks.