Hands-On Q-Learning with Python

States and actions

When first launched, your agent knows nothing about its environment and takes purely random actions.

As an example, suppose that a hypothetical self-driving car powered by a Q-learning algorithm notices that it's reached a red light, but it doesn't know that it's supposed to stop. It moves one block forward and receives a large penalty.

The car makes note of that penalty in the Q-table. The next time it encounters a red light, it looks at the Q-table when deciding what to do, and because the move-forward action in the state where it is stopped at a red light now has a lower reward value than any other action, it is less likely to decide to run the red light again.

Likewise, when it takes a correct action, such as stopping ...