Thank you!
I have not done a lot of work in gym. And to be honest, I do not understand why the concept of reward should come up in a logistic regression problem.
Granted that the input to the LSTM is sampled using a multinomial function, the LSTM should not have to think of long term rewards as in the reinforce episodic setting example that you gave above.
If you are saying that I should cast the regression learning problem into the form of a reinforcement learning problem, in my case, what would you suggest my reward should be before I backprop? Targets I predicted during the forward pass? How exactly would you recommend I call rloss.backward() as in the code snippet I gave early on?
Please pardon my many questions but I am a little confused about the introduction of a reward variable into the graph and a little more clarification would greatly suffice.