Tutorial on Self-Supervised RL

I gave a tutorial on self-supervised reinforcement learning at RLDM 2025.

Abstract. What fundamentally makes the reinforcement learning (RL) problem difficult is that the space of behaviors is large and complex. This tutorial introduces self-supervised reinforcement learning – a family of methods in which agents autonomously generate their own rewards (e.g., via intrinsic motivation) to learn skills that cover this large space of behaviors. These skills are not programmed in advance, but rather are discovered through exploring and experimenting, without using human demonstrations. By efficiently representing the space of possible behaviors, this set of skills (sometimes called a behavioral foundation model) can be leveraged to rapidly solve new tasks. Self-supervised RL has become an active area of research over the last decade. This tutorial will start by introducing algorithmic techniques for learning these skills, including ones based on goals and intrinsic rewards. We will then discuss the mathematical underpinnings of skills, and how skills can be used to solve downstream tasks. We will end by highlighting several open problems.

This site provides links to the tutorial materials:

Please reach out with any questions, comments, or suggestions! I’m sure that I’ve missed some important papers when doing my literature review.