O'Reilly logo

Deep Reinforcement Learning Hands-On by Maxim Lapan

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Prioritized replay buffer

The next very useful idea on how to improve DQN training was proposed in 2015 in the paper, Prioritized Experience Replay ([7] Schaul and others, 2015). This method tries to improve the efficiency of samples in the replay buffer by prioritizing those samples according to the training loss.

The basic DQN used the replay buffer to break the correlation between immediate transitions in our episodes. As we discussed in Chapter 6, Deep Q-Networks, the examples we experience during the episode will be highly correlated, as most of the time the environment is "smooth" and doesn't change much according to our actions. However, the SGD method assumes that the data we use for training has a i.i.d. property. To solve this problem, ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required