Reference

1. Expected Value and Markov Chains, Karen Ge
2. Probability and Random Processes With Applications to Signal Processing and Communications, 2nd Edition, Donald Childers, Scott Miller
3. The Fundamental Matrix of a Finite Markov Chain, Nick Foti
4. “Introduction to Stochastic Processes, Lecture 9 : Absorption and Reward”, The University of Texas at Austin, Fall 2019
5. Understanding Markov Decision Process (MDP), Rohan Jagtap
6. “Reinforcement Learning, Lecture 2: Markov Decision Processes”, University College London, Spring 2020