Hierarchical Reinforcement Learning with Hindsight

Understand

Reinforcement Learning (RL) algorithms can suffer from poor sample efficiency when rewards are delayed and sparse.

• We introduce a solution that enables agents to learn temporally extended actions at multiple levels of abstraction in a sample efficient and automated fashion.
• Our approach combines universal value functions and hindsight learning, allowing agents to learn policies belonging to different time scales in parallel.
• We show that our method significantly accelerates learning in a variety of discrete and continuous tasks.