Hands-On Q-Learning with Python

MDPs and state-action diagrams

Note that in the Markov chain examples we discussed, there is only one event that can happen in each state to cause the system to move to the next state. There is no list of actions and no decisions to make about what action to take. In a random walk, we flip the same fair coin each time, and each time we flip the coin, we have a new pair of states that we can potentially enter.

An MDP adds to a Markov chain the presence of a decision-making agent that has a choice of what action to take and rewards to receive, and provides feedback to the agent, affecting its behavior. Recall that an MDP doesn't require any knowledge of any previous states to make a decision on what action to take from the current state.