Which of the following best describes the relationship between Reinforcement Learning $($RL$)$ and Markov Decision Processes $($MDPs$)?$

Group of answer choices

RL is a subset of MDPs$,$ focusing specifically on learning optimal policies for decision$-$making tasks.

RL and MDPs are independent concepts, each addressing different aspects of decision$-$making problems.

RL and MDPs are equivalent terms, used interchangeably to describe the same concept in different contexts

MDPs are a subset of RL$,$ providing a formal framework for defining decision$-$making problems.