Summary

In this chapter, you learned the five fundamental principles of Artificial Intelligence from a Reinforcement Learning perspective. Firstly, an AI is a system that takes an observation (values, images, or any data) as input, and returns an action to perform as output (principle #1). Then, there is a reward system that helps it measure its performance. The AI will learn through trial and error based on the reward it gets over time (principle #2). The input (state), the output (action), and the reward system define the AI environment (principle #3). The AI interacts with this environment through the Markov decision process (principle #4). Finally, in training mode, the AI learns how to maximize its total reward by updating its parameters through the iterations, and in inference mode, the AI simply performs its actions over full episodes without updating any of its parameters – that is to say, without learning (principle #5).

In the next chapter, you will...