• Grokking RL: I didn't find an official resource 😢...

• Sutton and Barto: This book can be downloaded from:

  • Carnegie Mellon University's website
  • Stanford University's website
  • Direct PDF

• Thompson Sampling: This resource can be obtained from:

  • Stanford University's website
  • Direct PDF

.
├── Resources/
│   └── Books/
├── Week1/
│   ├── Assignment/
│   ├── {Solved Files}  # Files related to solved questions
│   └── README.md
├── Week2/
│   ├── Assignment/
│   ├── {Solved Files}  # Files related to solved questions
│   └── README.md
.
. (Continue similarly for other weeks)
.
├── .gitignore
├── CourseInfo.md
└── README.md

This week covers the basic topics of python required for Machine Learning and Reinforcement Learning. The basics of Reinforcement Learning are also covered in the books Grokking RL and Sutton and Barto.

This week covers the basics of Reinforcement Learning, mainly covered from Grokking RL. It also has an assignment to cover the basics of writing a game in python using the pygame module.

This week covers the algorithms to deal with Prediction and Control problems.

This week covers the implementation of the Multi-armed bandit problem implemented in python.

This week covers Temporal Difference Learning, Q Learning and Eligibility Traces along with a simple game and using Q-learning to "solve" the environment.

We have reached the ending! We will be implementing this paper to create a Chess Engine based on Deep RL.