When considering the capabilities of AI, we often compare its performance for a particular task with what humans can achieve. AI agents are now able to surpass human-level competency in more complex tasks. In this chapter, we will build an agent that learns how to play what is considered the most complex board game of all time: Go. We will become familiar with the latest deep reinforcement learning algorithms that achieve superhuman performances, namely AlphaGo, and AlphaGo Zero, both of which were developed by Google's DeepMind. We will also learn about Monte Carlo tree search, a popular tree-searching algorithm that is an integral component of turn-based game agents.
This chapter will cover the following topics:
- Introduction to Go and relevant research in AI
- Overview of AlphaGo and AlphaGo Zero
- The Monte Carlo tree search algorithm
- Implementation of...
