Reinforcement learning

Reinforcement learning is special in the sense that it doesn't require a dataset (see the following diagram). Instead, it involves an agent who takes actions, changing the state of the environment. After each step, it gets a reward or punishment, depending on the state and previous actions. The goal is to obtain a maximum cumulative reward. It can be used to teach the computer to play video games or drive a car. If you think about it, reinforcement learning is the way our pets train us humans: by rewarding our actions with tail-wagging, or punishing with scratched furniture.

One of the central topics in reinforcement learning is the exploration-exploitation dilemma—how to find a good balance between exploring new options and using what is already known:

Table 1.3: ML tasks: