In DQN, updating target network every N steps or slowly update every step is better? The use of target network is to reduce the chance of value divergence which could happen with off-policy samples trained with semi-gradient objectives. In Deep Q network, semi-gradient TD is used and with experience replay the training could diverge. 

Target network is a slow changing network designed to slowly track the main value network. In Mnih 2013, it was designed to match the main network every $N$ steps. There is another way which slowly updates the weight in the direction to match the main network every step. To someone, the latter is called Polyak updates. 

I have done some very limited experiments and seen that with the same update rate, e.g. $N=10$, Polyak update would update with the rate of 0.1, I usually see Polyak updates to give smoother progress and converge faster. My experiments are by no means conclusive. 

I would thence ask if it is known which one to perform better, converge faster or has smoother progress, in a wider range of tasks and settings?
Most of papers and my experience support hard update, once per N steps. N is usually very bug, range in between 10^4 to 10^6. DQN training is slow. But that depend on the problem. If you DQN converge with soft update with weight ~.1 (N~10) your problem could be very simple.