Reinforcement learning methods operate by
inferring the value function from online experience. However, in most
interesting applications, the state space is too large to enumerate
the value function. Some function approximator must be used to
compactly represent the value function. Many approaches have been
tried, but the most popular methods are neural nets, clustering,
nearest-neighbor methods, and CMAC.