Deep Q Network (DQN) is one of the very popular and widely used deep reinforcement learning (DRL) algorithms. In fact, it created a lot of buzz around the reinforcement learning (RL) community after its release. The algorithm was proposed by researchers at Google's DeepMind and achieved human-level results when playing any Atari game by just taking the game screen as input.
In this chapter, we will explore how DQN works and also learn how to build a DQN that plays any Atari game by taking only the game screen as input. We will look at some of the improvements made to DQN architecture, such as double DQN and dueling network architecture.
In this chapter, you will learn about:
- Deep Q Networks (DQNs)
- Architecture of DQN
- Building an agent to play Atari games
- Double DQN
- Prioritized experience replay
