2. Markov Decision Processes and Bellman Equations


This chapter will cover more of the theory behind reinforcement learning. We will cover Markov chains, Markov reward processes, and Markov decision processes. We will learn about the concepts of state values and action values along with Bellman equations to calculate previous quantities. By the end of this chapter, you will be able to solve Markov decision processes using linear programming methods.


In the previous chapter, we studied the main elements of Reinforcement Learning (RL). We described an agent as an entity that can perceive an environment's state and act by modifying the environment state in order to achieve a goal. An agent acts through a policy that ...

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