Learned representations and the search for unseen goals in reinforcement learning

A commenter says a paper changed how he thinks about learning by trial and error. He previously saw exploration as randomly finding a goal, then seeking shorter routes to it. Now, he says, learned representations map even an unseen goal into the environment’s structure, giving a policy somewhere to aim. If the goal isn’t there, the representations update and map it differently.