Deep Q-learning, or using deep Q-networks, is considered the most modern reinforcement learning technique. In this chapter, we will develop various deep Q-network models step by step and apply them to solve several reinforcement learning problems. We will start with vanilla Q-networks and enhance them with experience replay. We will improve robustness by using an additional target network and demonstrate how to fine-tune a Deep Q-Network. We will also experiment with dueling deep Q-networks and see how their value functions differs from other types of Deep Q-Networks. In the last two recipes, we will solve complex Atari game problems by incorporating convolutional neural networks into Deep Q-Networks.
The following recipes will be covered in this chapter:
- Developing deep Q-networks
- Improving DQNs with experience replay
- Developing double deep Q-Networks...
