Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection

Understand

This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz et al.

• 2017), and task-based curriculum learning (Bengio et al.
• 2009; Zaremba and Sutskever 2014).
• We propose a new method to integrate HRL, experience replay and effective subgoal selection through an implicit curriculum design based on human expertise to support sample-efficient learning and enhance interpretability of the agent's behavior.