Before diving into the different types of Reinforcement Learning and Algorithms, we should familiarize ourselves with the components of Reinforcement Learning. In the example of a baby exploring the world, the baby (agent) is in the real world (environment) and can be crying, feeling happy, or hungry (state). The baby can therefore choose to eat or sleep (action) and the baby is fulfilled if the baby gets to eat when he/she is hungry (reward). As mentioned at the start of the article, Reinforcement Learning involves exploration, and the output of Reinforcement Learning is an optimal policy. A policy describes the action to take at every state; akin to an instruction manual.