Summary

RL is one of the fundamental branches of machine learning and is currently one of the hottest, if not the hottest, areas of research and development. RL-based AI breakthroughs such as AlphaGo from Google’s DeepMind have further increased enthusiasm and interest in the field. This chapter provided an overview of RL and DRL and walked us through a hands-on exercise of building a DQN model using PyTorch.

RL is a vast field, and one chapter is not enough to cover everything. I encourage you to use the high-level discussions from this chapter to explore the details around those discussions. From the next chapter onward, we will focus on the practical aspects of working with PyTorch, such as model deployment, parallelized training, automated machine learning, and so on. In the next chapter, we discuss how to effectively train models in PyTorch using distributed training on CPUs, and GPUs, and using mixed precision training on GPUs.