Hi @tom
In case of using torch.normal, are there reasonable values to set .reinforce(reward) ? e.g. the gradients (backpropagated) in the next time step as the reward for each sample in current time step.
Concretely, say we have 50 initial samples, through NN, we get 50 outputs, then we compute mean and std, then generate 50 new samples, and feed this new samples through NN again, then we compute a loss.