This blog is mainly a notebook of Mathematical Foundations of Reinforcement Learning by Shiyu Zhao from Westlake University WindyLab.

You can find more about the book and related tutorial video at this link.

Reinforcement Learning (RL) can be described by the grid world example.

We place one agent in an environment, the goal of the agent is to find a good route to the target. Every cell/grid the agent placed can be seen as a state. Agent can take one action at each state according to a certain policy. The goal of RL is to find a good policy to guide the agent taking a sequence of acitons, travelling from the start place, moving from one state to another, and finally reach the target.