Reinforcement learners as we’ve seen them so far are not very realistic, however, because they don’t know what to do in a state unless they’ve been there before, and in the real world no two situations are ever exactly alike. We need to be able to generalize from previously visited states to new ones. Luckily, we already know how to do that: all we have to do is wrap reinforcement learning around one of the supervised learners we’ve met before, such as a multilayer perceptron.
