DQN Extensions

Since DeepMind published its paper on the deep Q-network (DQN) model (https://deepmind.com/research/publications/playing-atari-deep-reinforcement-learning) in 2015, many improvements have been proposed, along with tweaks to the basic architecture, which, significantly, have improved the convergence, stability, and sample efficiency of DeepMind's basic DQN. In this chapter, we will take a deeper look at some of those ideas.

Very conveniently, in October 2017, DeepMind published a paper called Rainbow: Combining Improvements in Deep Reinforcement Learning ([1] Hessel and others, 2017), which presented the seven most important improvements to DQN; some were invented in 2015, but others were very recent. In this paper, state-of-the-art results on the Atari games suite were reached, just by combining those seven methods. This chapter will go through all those methods. We will analyze the ideas behind them, alongside how they can be implemented and compared to the...