Finally, the pieces of RL are starting to really crystallize in my head. It only took til the end of CSC2547, and my first semester of grad school to get it!
Here is an ordering I found useful for studying:
- http://www.cs.toronto.edu/~rgrosse/courses/csc411_f18/ to lay the framework for machine learning.
- http://www.cs.toronto.edu/~rgrosse/courses/csc421_2019/ to lay the framework for deep learning. Policy gradient, REINFORCE is reinforced here ;)s
- http://karpathy.github.io/2016/05/31/rl/ to connect RL in the context of supervised learning; and the practicalities of training it
- https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html#notations to start my analysis of A3C, Actor-critic and all the other buzzwords you hear
- https://sergioskar.github.io/Actor_critics/ to give in-depth analysis and motivation on why actor critic is useful