In this chapter, we were introduced to DDQN, dueling network architectures, and the Rainbow DQN. We extended our previous DQN code to DDQN and dueling architectures and tried it out on Atari Breakout. We can clearly see that the average episode rewards are higher with these improvements, and so these improvements are a natural choice to use. Next, we also saw Google's Dopamine and used it to train a Rainbow DQN agent. Dopamine has several other RL algorithms, and the user is encouraged to dig deeper and try out these other RL algorithms as well.
This chapter was a good deep dive into the DQN variants, and we really covered a lot of mileage as far as coding of RL algorithms is involved. In the next chapter, we will learn about our next RL algorithm called Deep Deterministic Policy Gradient (DDPG), which is our first Actor-Critic RL algorithm and our first continuous...
