Summary
In this chapter, we have walked through and implemented a lot of DQN improvements that have been discovered by researchers since the first DQN paper was published in 2015. This list is far from complete. First of all, for the list of methods, I used the paper Rainbow: Combining improvements in deep reinforcement learning [Hes+18], which was published by DeepMind, so the list of methods is definitely biased to DeepMind papers. Secondly, RL is so active nowadays that new papers come out almost every day, which makes it very hard to keep up, even if we limit ourselves to one kind of RL model, such as a DQN. The goal of this chapter was to give you a practical view of different ideas that the field has developed.
In the next chapter, we will continue discussing practical DQN applications from an engineering perspective by talking about ways to improve DQN performance without touching the underlying method.
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