Chapter 9. Playing Doom with a Deep Recurrent Q Network

In the last chapter, we saw how to build an agent using a Deep Q Network (DQN) in order to play Atari games. We have taken advantage of neural networks for approximating the Q function, used the convolutional neural network (CNN) to understand the input game screen, and taken the past four game screens to better understand the current game state. In this chapter, we will learn how to improve the performance of our DQN by taking advantage of the recurrent neural network (RNN). We will also look at what is partially observable with the Markov Decision Process (MDP) and how we can solve that using a Deep Recurrent Q Network (DRQN). Following this, we will learn how to build an agent to play the game Doom using a DRQN. Finally, we will see a variant of DRQN called Deep Attention Recurrent Q Network (DARQN), which augments the attention mechanism to the DRQN architecture. 

In this chapter, you will learn the following topics:

• DRQN
• Partially...