In this chapter, we looked at building computer game playing agents using reinforcement learning. We went through the three main approaches: policy gradients, Q-learning, and model-based learning, and we saw how deep learning can be used with these approaches to achieve human or greater level performance. We would hope that the reader would come out of this chapter with enough knowledge to be able to use these techniques in other games or problems that they may want to solve. Reinforcement learning is an incredibly exciting area of research at the moment. Companies such as Google, Deepmind, OpenAI, and Microsoft are all investing heavily to unlock this future.
In the next chapter, we will take a look at anomaly detection and how the deep learning method can be applied to detect instances of fraud in financial transaction data.
