Supervised learning (also known as behavioral cloning) if the agent is learning what to do in an observe-act cycle) is a type of decision making method.
- provide the agent with some examples
- use an automated learning algorithm to generalize from the example
This is good for typically representative situations, but if you are throwing an agent into a completely unfamiliar situation, supervised learning cannot perform better.
Disadvantages
- the labeled data is finite
- limited by the quality of performance in the training data
- interpolation between states are finite