Class Information (Informations sur les classes)

We are using Discord for outside-of-lecture-time discussions. Nous utilisons Discord pour les discussions en-dehors-des-heures de cours.

Programming Assignments / Attributions de Programmation

Course Project /Project de cours

Learning Resources / Ressources pédagogiques

Class Schedule / Calendrier des cours

Course Readings / Lectures du cours

Course Discussion / Discussion du cours

Rules for ChatGPT (and other generative model) Usage / Règles d'utilisation de ChatGPT (et d'autres modèles génératifs)

FAQ

Learning methods such as deep reinforcement learning have shown success in solving simulated planning and control problems but struggle to produce diverse, intelligent behaviour on systems that interact in the real world (robots). This class aims to discuss these limitations and study methods to overcome them and enable agents capable of training autonomously, becoming learning and adapting systems that require little supervision. By the end of the course, each student should have a solid grasp of different techniques to train agents to accomplish tasks in the real world. These techniques covered in the course include but are not limited to reinforcement learning, batch RL, multi-task RL, model-based RL, Sim2Real, hierarchical RL, goal-conditioned RL, multi-Agent RL, the fragility of RL, meta-level decision making and learning reward functions.