In Chapter 5, Tabular Learning and the Bellman Equation, you became familiar with the Bellman equation and the practical method of its application called value iteration. This approach allowed us to significantly improve our speed and convergence in the FrozenLake environment, which is promising, but can we go further? In this chapter, we will apply the same approach to problems of much greater complexity: arcade games from the Atari 2600 platform, which are the de facto benchmark of the reinforcement learning (RL) research community.
To deal with this new and more challenging goal, in this chapter, we will:
- Talk about problems with the value iteration method and consider its variation, called Q-learning.
- Apply Q-learning to so-called grid world environments, which is called tabular Q-learning.
- Discuss Q-learning in conjunction with neural networks (NNs). This combination has the name deep Q-network (DQN).
At the end of the chapter, we will...