In reinforcement learning an action is executed for each data point. The action’s execution generates a reward signal that indicates how good the decision was.
This algorithm then modifies its action strategy to enhance the possibility of the highest reward against the specific environment.
Due to the missing training data, the agent learns from repeating experience. It collects the knowledge by keeping record (“this action was good, that action was bad”). It learns via trial and error as it attempts modifying and ...
