Before revision: Autonomous agentsusing novelty based goal exploration are often efficient in environments that require exploration. However, they get attracted to various forms of distracting unlearnable regions.
After revision: Designing agents, capable of learning autonomously a wide range of skills is critical in order to increase the scope of reinforcement learning. It will both increase the diversity of learned skills and reduce the burden of manually designing reward functions for each skill. Self-supervised agents, setting their own goals, and trying to maximize the diversity of those goals have shown great promise towards this end. However, a currently known limitation of agents trying to maximize the diversity of sampled goals is that they tend to get attracted to various forms of distracting unlearnable regions.