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I have read the code and I found that when apply gradients, it apply all variable in the share networks which is written in line 343 of "ProgressiveNN_atari_2.py" self.apply_gradients = master.shared_opt.apply_gradients( zip(self.accum_grads, master.shared_net.get_vars()), global_step=master.global_step)
The paper says that when update the weights, it should frozen the weights of previous learned policies, but I can't find which step frozens the previous weights.
The text was updated successfully, but these errors were encountered:
I have read the code and I found that when apply gradients, it apply all variable in the share networks which is written in line 343 of "ProgressiveNN_atari_2.py"
self.apply_gradients = master.shared_opt.apply_gradients( zip(self.accum_grads, master.shared_net.get_vars()), global_step=master.global_step)
The paper says that when update the weights, it should frozen the weights of previous learned policies, but I can't find which step frozens the previous weights.
The text was updated successfully, but these errors were encountered: