While deep reinforcement learning (RL) is becoming an integral part of good
decision-making in data science, it is still plagued with sample inefficiency.
This can be challenging when applying deep-RL in real-world environments where
physical interactions are expensive and can risk system safety. To improve the
sample efficiency of RL algorithms, this paper proposes a novel human-inspired
framework that facilitates fast exploration and learning for difficult RL
tasks. The main idea is to first provide the learning agent with simpler but
similar tasks that gradually grow in difficulty and progress toward the main
task. The proposed method requires no pre-training phase. Specifically, the
learning of simpler tasks is only done for one iteration. The generated
knowledge could be used by any transfer learning, including value transfer and
policy transfer, to reduce the sample complexity while not adding to the
computational complexity. So, it can be applied to any goal, environment, and
reinforcement learning algorithm - both value-based methods and policy-based
methods and both tabular methods and deep-RL methods. We have evaluated our
proposed framework on both a simple Random Walk for illustration purposes and
on more challenging optimal control problems with constraint. The experiments
show the good performance of our proposed framework in improving the sample
efficiency of RL-learning algorithms, especially when the main task is
difficult.

本文提出了一种基于人类启发的框架以提高采样效率，其中通过逐步提供简单但相似的任务来适应复杂的强化学习任务，并且使用任何转移学习方法来减少样本复杂性而不增加计算复杂性，实验表明该框架能够在优化问题方面表现出良好的性能。