# Do smaller loss values during DQN training produce better policies?

During the training of DQN, I noticed that the model with prioritized experience replay (PER) had a smaller loss in general compared to a DQN without PER. The mean squared loss was an order of magnitude $$10^{-5}$$ for the DQN with PER, whereas the mean squared loss was an order of magnitude $$10^{-2}$$.

Do the smaller training errors have any effect on executing the final policy learned by the DQN?