The challenges we listed previously have no simple solutions. However, there has been an effort in trying to overcome them and to come up with novel strategies to improve efficiency, generalization, and stability. Two of the most widespread and promising techniques that focus on efficiency and generalization are unsupervised reinforcement learning and transfer learning. In most cases, these strategies work in symbiosis with the deep reinforcement learning algorithms that we developed in the previous chapters.
Advanced techniques
Unsupervised RL
Unsupervised RL is related to the usual unsupervised learning in how both methods don't use any source of supervision. While in unsupervised learning the data isn't labeled...
