Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards

NeurIPS 2019 Siyuan LiRui WangMinxue TangChongjie Zhang

Hierarchical Reinforcement Learning (HRL) is a promising approach to solving long-horizon problems with sparse and delayed rewards. Many existing HRL algorithms either use pre-trained low-level skills that are unadaptable, or require domain-specific information to define low-level rewards... (read more)

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