Background (may be skipped):
In training neural networks, usually stochastic gradient descent (SGD) is used: instead of computing the network's error on all members of the training set and updating the weights by gradient descent (which means waiting a long time before each weight update), use each time a minbatch of members, and treat the resulting error as an unbiased estimation of the true error.
In reinforcement learning, sometimes Q-learning is implemented with a neural network (as in deep Q-learning), and experience replay is used: Instead of updating the weights by the previous (state,action,reward) of the agent, update using a minibatch of random samples of old (states,actions,rewards), so that there is no correlation between subsequent updates.
Is the following assertion correct?: When minibatching in SGD, one weights update is performed per the whole minibatch, while when minibatching in Q-learning, one weights update is performed per each member in the minibatch?