Second, instead of requiring supervised signals at each time step, deep reinforcement learning model optimizes its policy based on an overall reward signal.
it learns to directly optimize the overall evaluation metrics, which is more effective than models that learn with loss functions that heuristically define the goodness of a particular single decision.