Before revision:  However, they get attracted to various forms of distracting unlearnable regions. To solve this problem, absolute learning progress (ALP)has been used in reinforcement learning agents with predefined goal features and access to expert knowledge.
After revision:  However, they get attracted to noise or more generally to parts of the environments that cannot be controlled (distractors). When agents have access to predefined goal features and access to expert knowledge.