 A more efficient approach where the Q(s,a) computation is effectively shared among the neurons in the network. Only a single forward pass is required to find the best action to take in any given state.
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Formulated in this way, the update algorithm becomes:

1. Experience a \\(s\_t, a\_t, r\_t, s\_{t+1}\\) transition in the environment.
2. Forward \\(s\_{t+1}\\) to evaluate the (fixed) target \\(y = r\_t + \gamma \max\_a f\_{\theta}(s\_{t+1})\\).
3. Forward \\(f\_{\theta}(s\_t)\\) and apply L2 regres