In supervised learning we have assumed that there is a target output value for each input value. However, in many situations, there is less detailed information available. In extreme situations, there is only a single bit of information after a long sequence of inputs telling whether the output is right or wrong.
Reinforcement learning is one method developed to deal with such situations.
However the feedback signal is only evaluative, not instructive. Reinforcement learning is often called learning with a critic as opposed to learning with a teacher . Learning from Interaction Humans learn by interacting with the environment. When a baby plays, it waves its arms around, touches things, tastes things, etc. There is no explicit teacher but there is a sensori-motor connection to its environment.