Fundamentals of Reinforcement Learning

In RL, the main goal is to learn from interaction. We want agents to learn a behavior, a way of selecting actions in given situations, to achieve some goal. The main difference between classical programming or planning is that we do not want to code the planning software explicitly on our own, as this would require a great effort; it can be very inefficient and even impossible. The RL discipline was born precisely for this reason.

RL agents start (usually) with no idea of what to do. They typically do not know the goal, they do not know the game's rules, and they do not know the dynamics of the environment or how their actions influence the state.

There are three main components of RL: perception, actions, and goals.

Agents should be able to perceive the current environment state to deal with a task. This perception, also called observation, might be different from the actual environment state, can be subject to noise, or can be...