sion loss on the dimension \\(a\_t\\) of the output which we want to equal to \\(y\\). Due to the L2 loss, the gradient has a simple form where the predicted value is simply subtracted from \\(y\\). The output dimensions corresponding to the other actions have zero gradient. 
4. Backpropagate the gradient and perform a parameter update.

**Experience Replay Memory**. An important contribution of the Mnih et al. papers was to suggest an experience replay memory. This is loosely inspired by the brain, and in 