From a technical perspective, whereas supervised and unsupervised learning appears at opposite ends of the spectrum, RL exists somewhere in the middle. It's not supervised learning because the training data comes from the algorithm deciding between exploration and exploitation. And it's not unsupervised because the algorithm receives feedback from the environment. As long as you are in a situation where performing an action in a state produces a reward, you can use reinforcement learning to discover a good sequence of actions to take the maximum expected rewards.
The goal of an RL agent will be to maximize the total reward that it receives in the long run. The third main sub element is the value function.
While the rewards determine an immediate desirability of the states, the values indicate the long-term desirability of states, taking into account the states that may follow and the available rewards in these states. The value function is specified with respect to the...
