It is possible to use neural networks to learn about data that contains neither target outputs nor class labels. An autoassociator network thus tries to learn the identity function. Together with the linear bottleneck layer, this gives a network with at least 3 hidden layers. Such a deep network should be preconditioned if it is to learn successfully. Like all neural network architecture parameters, the dimension d of the embedding is an important but difficult choice.
Reinforcement learning provides ways to get a continuous error signal in such situations.
For large problems, however, it is often impossible to keep such a large table in memory, let alone learn its entries in reasonable time. In such cases a neural network can provide a compact approximation of the Q-value function. This means that search must be used to determine the value of the best successor state. TD-Gammon is now the world's strongest backgammon program, and plays at the level of human grandmasters.