Hands-On Reinforcement Learning with Python

Summary

In this chapter, we have learned about one of the very popular deep reinforcement learning algorithms called DQN. We saw how deep neural networks are used to approximate the Q function. We also learned how to build an agent to play Atari games. Later, we looked at several advancements to the DQN, such as double DQN, which is used to avoid overestimating Q values. We then looked at prioritized experience replay, for prioritizing the experience, and dueling network architecture, which breaks down the Q function computation into two streams, called value stream and advantage stream.

In the next chapter, Chapter 9, Playing Doom with Deep Recurrent Q Network, we will look at a really cool variant of DQNs called DRQN, which makes use of ...