The Markov Decision Process (MDP) provides a mathematical framework for solving the reinforcement learning (RL) problem. Almost all RL problems can be modeled as MDP. MDP is widely used for solving various optimization problems. In this chapter, we will understand what MDP is and how can we use it to solve RL problems. We will also learn about dynamic programming, which is a technique for solving complex problems in an efficient way. 

In this chapter, you will learn about the following topics:

• The Markov chain and Markov process
• The Markov Decision Process
• Rewards and returns
• The Bellman equation
• Solving a Bellman equation using dynamic programming
• Solving a frozen lake problem using value and policy iteration