I'm using Python and tensorflow to implement a Deep Q-learning with experience replay in a continous action and state spaces and I have used two neural networks to approximate both the policy function and the Q-function. While for the Q-function I have a target so I can update the model minimizing the loss function, the policy update pseudocode does not include a target which I can use to fit the model but only a gradient ascent step, as the following
How can I use tensorflow and a sequential model in python to make this update?
@nbro
, so that I receive a notification of your message/comment. $\endgroup$ – nbro♦ May 23 '20 at 11:18