I have a question. If I want to add dropout into the network for policy gradient, how can I do that?
I think in order to do that, I need to completely change the code. Right now the workflow is as follows.
Having state -> do a forward computation -> having the output -> compute the gradient -> create a new input, output to train the network -> perform training the network with the <input, output> for one epoch -> repeating again.
However, to add dropout we need to change the workflow as follows:
Having state -> do a forward computation -> having the output -> compute the gradient -> backpropogate the gradient -> modifying network parameters -> repeating.
This would really complicate for an automatic differentiation system like Keras, I think. Any idea?
Hi all,
Thanks for your amazing project!
I have a question. If I want to add dropout into the network for policy gradient, how can I do that? I think in order to do that, I need to completely change the code. Right now the workflow is as follows. Having state -> do a forward computation -> having the output -> compute the gradient -> create a new input, output to train the network -> perform training the network with the <input, output> for one epoch -> repeating again.
However, to add dropout we need to change the workflow as follows: Having state -> do a forward computation -> having the output -> compute the gradient -> backpropogate the gradient -> modifying network parameters -> repeating.
This would really complicate for an automatic differentiation system like Keras, I think. Any idea?
Thanks a lot for your help!
Best,