Title: Learning Plannable Representations and Planning with Learnable Skills

Abstract: Reinforcement learning provides a powerful framework for automatically learning control policies for autonomous systems. However, using RL in real-world settings, particularly complex and safety-critical domains such as autonomous driving, is often impractical due to the need for costly and dangerous exploration. In this talk, I will discuss how the framework of offline reinforcement learning can drastically expand the applicability of RL to real-world settings, discuss the fundamentals of offline RL algorithms and recent innovations, and some of our recent experience applying RL to problems in robotics and mobility.