Reinforcement learning is a different paradigm in machine learning where an agent tries to learn to behave optimally in a defined environment by making decisions/actions and observing the outcome of that decision. So, in the case of reinforcement learning, the agent is not really from some given dataset, but rather, by interacting with the environment, the agent tries to learn by observing the effects of its actions. The environment is defined in such a way that the agent gets rewards if its action gets it closer to the goal.
Humans are known to learn in this way. For example, consider a child in front of a fireplace where the child is the agent and the space around the child is the environment. Now, if the child moves its hand towards the fire, it feels the warmth, which feels good and, in a way, the child (or the agent) is rewarded for the action of moving...
