https://github.com/tallamjr/stanford-cs234

Stanford CS234 : Reinforcement Learning
https://github.com/tallamjr/stanford-cs234

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Stanford CS234 : Reinforcement Learning

• Host: GitHub
• URL: https://github.com/tallamjr/stanford-cs234
• Owner: tallamjr
• Created: 2019-10-03T12:47:24.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-10-03T12:47:54.000Z (about 5 years ago)
• Last Synced: 2024-05-01T12:44:05.807Z (5 months ago)
• Language: Python
• Size: 69.3 MB
• Stars: 76
• Watchers: 3
• Forks: 46
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Stanford CS234 : Reinforcement Learning

## Course Description

To realize the dreams and impact of AI requires autonomous systems that learn to

make good decisions. Reinforcement learning is one powerful paradigm for doing

so, and it is relevant to an enormous range of tasks, including robotics, game

playing, consumer modeling and healthcare. This class will provide a solid

introduction to the field of reinforcement learning and students will learn

about the core challenges and approaches, including generalization and

exploration. Through a combination of lectures, and written and coding

assignments, students will become well versed in key ideas and techniques for

RL. Assignments will include the basics of reinforcement learning as well as

deep reinforcement learning — an extremely promising new area that combines deep

learning techniques with reinforcement learning. In addition, students will

advance their understanding and the field of RL through a final project.

## 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 project and the exam).

Implement in code common RL algorithms (as assessed by the homeworks).

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 homeworks 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).

### References

* [Course webpage](http://web.stanford.edu/class/cs234/index.html)

* [Youtube

    videos](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u)