Value-based Methods in Deep Reinforcement Learning

There are three types of common machine learning approaches: 1) supervised learning, where a learning system learns a latent map based on labeled examples, 2) unsupervised learning, where a learning system establishes a model for data distribution based on unlabeled examples, and 3) Reinforcement Learning, where a decision-making system is trained to make optimal decisions. From the designer's point-of-view, all kinds of learning are supervised by a loss function. The sources of supervision must be defined by humans. One way to do this is by the loss function. In supervised learning, the ground truth label is provided.