So far, in this chapter, we went through the best practices when developing deep RL algorithms and the challenges behind RL. We also saw how unsupervised RL and meta-learning can alleviate the problem of low efficiency and bad generalization. Now, we want to show you the problems that need to be addressed when employing an RL agent in the real world, and how the gap within a simulated environment can be bridged.

Designing an agent that is capable of performing actions in the real world is demanding. But most reinforcement learning applications need to be deployed in the world. Thus, we have to understand the main challenges that we face when dealing with the complexity of the physical world and consider some useful techniques.