Every scientific and engineering field has its own assumptions and limitations. In the previous section, we discussed supervised learning, in which such assumptions are the knowledge of input-output pairs. You have no labels for your data? You need to figure out how to obtain labels or try to use some other theory. This doesn't make supervised learning good or bad; it just makes it inapplicable to your problem.
There are many historical examples of practical and theoretical breakthroughs that have occurred when somebody tried to challenge rules in a creative way. However, we also must understand our limitations. It's important to know and understand game rules for various methods, as it can save you tons of time in advance. Of course, such formalisms exist for RL, and we will spend the rest of this book analyzing them from various angles.
The following diagram shows two major RL entities—agent and environment—and their communication channels...