sample inefficiency. This poses a risk to system safety and can be costly in real-world
environments with physical interactions. This paper proposes a human-inspired framework
to improve the sample efficiency of RL algorithms, which gradually provides the learning
agent with simpler but similar tasks that progress toward the main task. The proposed
method does not require pre-training and can be applied to any goal, environment, and RL …