Data-Efficient Hierarchical Reinforcement Learning -- HIRO

Traditional reinforcement learning algorithms have achieved encouraging success in recent years. Their nature of reasoning on the atomic scale, however, makes them hard to scale to complex tasks. Hierarchical Reinforcement Learning(HRL) introduces high-level abstraction, whereby the agent is able to plan on different scales. In this post, we discuss an HRL algorithm proposed by Ofir Nachum et al. in Google Brain at NIPS 2018. The algorithm, known as HIerarchical Reinforcement learning with Off-policy correction(HIRO), is designed for goal-directed tasks, in which the agent tries to reach some goal state.