Meta-Reinforcement Learning - Boosting RL Generalization
Reinforcement Learning (RL) enables agents to learn optimal behaviors through trial and error but often struggles to generalize to new situations. Meta-Reinforcement Learning (Meta-RL) addresses this limitation by leveraging prior experience to help agents adapt more quickly to novel tasks. This tutorial will cover the fundamentals of deep RL and meta-learning, including an introduction to MAML applied to RL. The hands-on session will use a grid environment to explore how policies trained on a single environment, with domain randomization, or via meta-learning respond to environmental perturbations.
- Date: September 17, 2025
- Time: Theory: 14:00 - 14:50 / Coffee 14:50 - 15:10 / Lab 15:10 - 16:00
- Location: Nancy-Salle A008 Jean Legras
- Instructor: Franco Terranova, Ph.D. Candidate