Building the environment
This time, as opposed to some of the other practical sections in this book, we don't have to specify any variables or make any assumptions. We can just go straight to the three crucial steps present in every deep Q-learning project:
- Defining the states
- Defining the actions
- Defining the rewards
Let's begin!
Defining the states
In every previous example, our states were a 1D vector that represented some values that define the environment. For example, for our self-driving car we had the information gathered from the three sensors around the car and the car's position. All of these were put into a single 1D array.
But what if we want to make something slightly more realistic? What if we want the AI to see and gather information from the same source as we do? Well, that's what we'll do in this chapter. Our AI will see exactly the same board as we see when playing Snake!
The state...
