Week 1 Overview
Course Introduction, Imitation Learning
Week 2 Overview
Policy Gradient
Week 3 Overview
Deep RL
Week 4 Overview
Model-Based Reinforcement Learning
Week 6 Overview
Offline Reinforcement Learning
Week 7 Overview
Beyond traditional DRL
Week 8 Overview
Challenges in real DRL
- Homework 3: Q-learning and Actor-Critic Algorithms
- Best homework 2 presentation
- Best homework 3 presentation
- Instadeep presentations with Arthur Flajolet and Philomène Chagniot
Homeworks
Check this part regularly to avoid being late on your homework.
Lecture Slides
See Syllabus for more information.
- Lecture 1: Introduction and Course Overview-
- Lecture 2: Supervised Learning of Behaviors
- Lecture 3: Introduction to Pytorch
- Lecture 4: Introduction to Reinforcement Learning
- Lecture 5: Policy Gradients
- Lecture 6: Actor-Critic Algorithms
- Lecture 7: Value Function Methods
- Lecture 8: Deep RL with Q-Functions
- Lecture 9: Advanced Policy Gradients
- Lecture 10: RL for temporal decisions
- Lecture 11: Optimal Control and Planning
- Lecture 12: Model-Based Reinforcement Learning
- Lecture 13: Model-Based Policy Learning
- Lecture 14: Exploration (Part 1)
- Lecture 15: Exploration (Part 2)
- Lecture 16: Offline Reinforcement Learning (Part 1)
- Lecture 17: Offline Reinforcement Learning (Part 2)
- Lecture 18: Reinforcement Learning Theory Basics
- Lecture 19: Variational and Generative Models
- Lecture 20: Inference and Control
- Lecture 21: Inverse Reinforcement Learning
- Lecture 22: Transfer and Multi-Task Learning
- Lecture 23: Meta-Learning
- Lecture 24: Challenges and Open Problems
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