Summary

In this chapter, we have learned how to implement a dueling DQN in detail. We started off with the basic environment wrapper functions for preprocessing our game screens and then we defined the QNetworkDueling class. Here, we implemented a dueling Q Network, which splits the final fully connected layer of DQN into a value stream and an advantage stream and then combines these two streams to compute the q value. Following this, we saw how to create a replay buffer, which is used to store the experience and samples a minibatch of experience for training the network, and finally, we initialized our car racing environment using OpenAI's Gym and trained our agent. In the next chapter, Chapter 13, Recent Advancements and Next Steps, we will see some of the recent advancements in RL.