Supervised Learning analyses the training data and produces a generalized formula.
In Reinforcement Learning basic reinforcement is defined in the model Markov’s Decision process.
In Supervised Learning, each example will have input objects and output with desired values.
In Reinforcement Learning, the agent interacts with the environment in discrete steps, and receives a reward for ever observation. The goal is to collect as many rewards as possible to make more observations.
