Start date/Date de début: Jan. 8
When/Quand:
Where/Où:
Office hours: Wednesday 4:00-5:00 pm at UdeM AA3248.
TAs
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
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.