Deep Reinforcement Learning in Action

Part 2. Above and beyond

After mastering the basics of deep reinforcement learning in part 1, part 2 delves into a variety of more advanced techniques and tackles more sophisticated environments. The chapters in this part can more or less be approached in any order as they do not rely on each other. However, each chapter tends to be more complex than the previous one, so it still may be better to go in sequence.

In chapter 6 we’ll introduce an alternative framework for training neural networks using ideas borrowed from the biological sciences. In particular, we’ll adapt Charles Darwin’s theory of evolution by natural selection for machine learning.

In chapter 7 we’ll show that most approaches to reinforcement learning are impoverished in how ...