This repository provides complimentary coding exercises and solutions for RL Learning Roadmap. The coding exercises format is based on the awesome WildML Learning Reinforcement Learning course by Denny Britz. Exercises focus on implementing algorithms (the meat of RL).

Given many RL courses in the community in Python, I create some coding exercises for MATLAB users. If you don't use MATLAB but want to follow RL Learning Roadmap, rework these exercises in your favorite framework.

This is not an official MathWorks product. For online courses from MathWorks, see https://matlabacademy.mathworks.com/.

Coding Exercises requires Reinforcement Learning Toolbox but you can always reimplement from scratch.

• Chapter 1 - Dynamic Programming
• Chapter 2 - Temporal-Difference (TD) Learning (WIP)
• Chapter 3 - Function Approximation (WIP)
• Chapter 4 - Policy Gradient (WIP)
• Chapter 5 - Advanced Policy Gradient (WIP)
• Chapter 6 - Partially Observable Environment (WIP)
• Chapter 7 - Model-based (WIP)

Learning materials referred from:

• Reinforcement Learning Toolbox, The MathWorks
• Reinforcement Learning: An Introduction (textbook), Sutton and Barto
• Deep Reinforcement Learning (course), UC Berkeley
• OpenAI Spinning Up(textbook/blog)
• WildML Learning Reinforcement Learning (python course with exercises/solutions), Denny Britz
• MATLAB RL Tech Talks (videos), The MathWorks
• David Silver’s RL course
• Simple Reinforcement Learning (blog), Arthur Juliani
• Deep Learning Specialization Coursera (course), Andrew Ng (you can audit for free, highly recommend course 1 + 2 to get Deep Learning foundations)