Interactive session, Thursday March 21
Weekly lecture:
Slides:
Reinforcement learning (part 1)
Reinforcement Learning (part 2)
Videos:
- Introduction to Reinforcement Learning (RL)
- Policies and states in RL
- Rewards in RL
- Defining a policy in RL
- Learning algorithms in RL
- Q-learning example
- On-policy and off-policy learning
Readings:
Marsland Chapter 11
Optional Reading
The readings below go beyond the syllabus for this class, so do not worry if you don't understand everything. But, it can provide some interesting perspectives if you want to dig deeper into RL.
From Q-Learning to Deep Q-Learning: https://towardsdatascience.com/reinforcement-learning-tutorial-part-3-basic-deep-q-learning-186164c3bf4
The Paths Perspective on Value Learning: https://distill.pub/2019/paths-perspective-on-value-learning/
(Advanced) Article showing important limitations of state-of-the-art Reinforcement Learning: https://thegradient.pub/why-rl-is-flawed/