Learning for Interactive Agents

One of the key challenges in developing intelligent and autonomous learning agents is their ability to effectively interact with humans. In this tutorial, we plan to cover the theoretical and practical foundations of interactive agents. Specifically, in the first part of the tutorial, we will focus on models of human behavior in isolation, how these models can be used for effective coordination and how they can be optimized for influencing the partner. In the second part of the tutorial, we will continue by introducing co-adaptation settings, where the human preferences are non-stationary and they adapt, and we will discuss how this leads to emergence of new norms, conventions, and equilibria. Finally, we will wrap up by introducing approaches for inferring human partner preferences using a range of offline and online sources of data present in interactive domains. Throughout this tutorial, we will also go over concrete examples from applications in autonomous driving, mixed-autonomy traffic network, personal robotics, and multi-agent games.