Overview

Recent advances in reinforcement learning (RL) have led to remarkable performance of AI systems in challenging application domains, e.g., robotics and game play (Go, etc.). However, these feats have largely been limited to well-specified tasks with known dynamics and predictable outcomes. These limitations can be addressed by designing AI systems that emerge from the complementary abilities of humans and machines by enabling close collaborations between them. For instance, in autonomous driving, an AI auto-pilot could hand over control to the human driver in safety-critical situations. To enable such collaboration, there has been a surge of interest in developing novel RL techniques that effectively and efficiently learn with-and-from people in complex real-world environments.

In this tutorial we provide an overview of the recent advances in designing RL techniques for human-AI collaborations. The contents are loosely based on our workshop "Human-AI Collaboration in Sequential Decision-Making" at ICML'21.