Hands-On Q-Learning with Python

The decision-making process

A learning agent's high-level algorithm looks like the following:

1. Take note of what state you're in.
2. Take an action based on your policy and receive a reward.
3. Take note of the reward you received by taking that action in that state.

We can express this mathematically using a Markov decision process (MDP). We'll discuss MDPs in more detail throughout the book. For now, we need to be aware that an MDP describes an environment for RL in which the current state tells us everything we need to know about future states.

What this means, in short, is that if we know the current state of the environment in an MDP, we don't need to know anything about any past states in order to determine what future states will be, ...