Application of Deep Reinforcement Learning I'm new to deep learning, and especially to reinforcement learning. I would like to know if it's possible to predict which combination of hashtags (from a subset of chosen hashtags) would produce the most likes for a certain image. 

Is it possible to have a convolutional neural network with each hashtag as a label, and take something like reward = likes / followers as a reward in a reinforcement learning like scenario?

In what other way could I face this problem? My goal isn't to predict the amount of likes, but to maximize the probability to get the most likes.

I chose this title because I think the answer could actually be question agnostic: I could use the same knowledge to define which combination of stocks would maximize my investment.

Thanks in advance.
This honestly sounds more like a supervised learning problem.
For reinforcement learning to work, you would need a model that can constantly return values for a given input.

With social media, that would mean 


a) posting the same image with all kinds of different hashtags and expecting people to give the most appropiate hashtags likes in a short amount of time. This will not happen.
b) searching for all occurrences of an image with different hashtags. This is basically supervised learning.


I recommend finding a social media dataset first. Try to group all occurrences of an image and find the median of likes for each hashtag. Don't forget to compensate for follower count, as more popular posters will get more likes on average regardless of hashtag. Store the best hashtag as label for the given image.

You now have Y different hashtags for X different images.

From now, you can treat it as classification problem. X is your sample count, Y is the amount of possible outputs. If you don't want to write your own loss function and only want to predict the single best hashtag, use cross entropy as loss function.

Of course you still have to choose your social media dataset, appropriate images for training and NN structure, but I hope this helps as a general approach.