 make Q Learning with function approximation tracktable in practical problems:

**Modeling Q(s,a)**. First, as mentioned we are using a function approximator to model the Q function: \\(Q(s,a) = f\_{\theta}(s,a)\\). The natural approach to take is to perhaps have a neural network that takes as inputs the state vector \\(s\\) and an action vector \\(a\\) (e.g. encoded with a 1-of-k indicator vector), and output a single number that gives \\(Q(s,a)\\). However, this approach leads to a practical problem becau