1. A method of employing an instance weighted learning (IWL) machine learning model to train a classifier, the method comprising: determining a quality value for each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, each quality value being determined by determining a reward of a current machine learning training instance in the temporal sequence and determining a discounted portion of the reward that is added to each of the previous machine learning training instances in the temporal sequence, each of the machine learning training instances including a state-action pair; associating the corresponding determined quality value with each of the machine learning training instances; and training, using reinforcement learning, a classifier using each of the machine learning training instances, with each of the machine learning training instances weighted during the training based on its associated quality value using a weighting factor that is a function of its associated quality value, such that the training of the classifier is influenced more by a machine learning training instance with a higher quality value than by a machine learning training instance with a lower quality value.