#concept-pamphlet #todo: learnnn from cs234
https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html
https://github.com/Nasdin/ReinforcementLearning-AtariGame
CS234 https://web.stanford.edu/class/cs234/
https://web.stanford.edu/class/cs234/CS234Win2023/index.html
Convex optimization?
Learning Outcomes
By the end of the class students should be able to:
- Define the key features of reinforcement learning that distinguishes it from AI and non-interactive machine learning (as assessed by the exam).
- Given an application problem (e.g. from computer vision, robotics, etc), decide if it should be formulated as a RL problem; if yes be able to define it formally (in terms of the state space, action space, dynamics and reward model), state what algorithm (from class) is best suited for addressing it and justify your answer (as assessed by the exam).
- Implement in code common RL algorithms (as assessed by the assignments).
- Describe (list and define) multiple criteria for analyzing RL algorithms and evaluate algorithms on these metrics: e.g. regret, sample complexity, computational complexity, empirical performance, convergence, etc (as assessed by assignments and the exam).
- Describe the exploration vs exploitation challenge and compare and contrast at least two approaches for addressing this challenge (in terms of performance, scalability, complexity of implementation, and theoretical guarantees) (as assessed by an assignment and the exam).
^this looks useful