text: Although I will not dwell on the details in this answer (and you can find more details here), depending on the problem or the RL algorithms that you want to use to solve the problem, one definition may be more interesting or useful than another.
  In evolutionary algorithms (EA), such as genetic algorithms (GA) or genetic programming (GP), the solutions are generally known as individuals, which can be represented in different ways depending on the specific approach.
  However, in all cases, we often distinguish between genotypes (aka genomes or chromosomes), which are representations of the actual solutions to a problem, which are more amenable to changes (i.e. to the mutation and crossover operations), and phenotypes, which are the actual solutions to the problem. The reason why we use genotypes is that, often, it's easier to find solutions in a different but related space rather than in the original space of solutions, although some EAs can also just work with phenotypes.
  To be more concrete, in traditional genetic algorithms, the genotypes are binary vectors $\{0, 1 \}^N$, which restricts the space of possible solutions, which, in certain cases, it's a good thing, because the original space may be too huge (so it may be harder to converge to a good solution). These binary vectors can then be converted to the actual solutions using some encoding-decoding mechanism that we need to specify depending on the problem. More precisely, maybe each element of a binary vector corresponds to some binary choice in some game where you need to take a sequence of choices, and each time you can either say "yes" or "no".