I'm attempting to train a neural network to maximise a function, where the network takes a time series as input. At each time step it will make a decision based on what it thinks will maximise the reward function, however I have a small portion of the data which is labelled with a good action which would be valuable to incorporate.
Does anyone have any suggestions as to how to blend the two approaches?
I was thinking of either pretraining using the labelled data, or at each timestep calculating two weight updates - one which encourages matching the labels and another from gradient ascent on the reward function. I would weight these dependent on time, so that the update based on labelled data has more influence at the start of training and less towards the end. Any pointers/papers would be appreciated!
(combining supervised and unsupervised learning is the gist of what I'm looking for)