Data-Efficient Hierarchical Reinforcement Learning

NeurIPS 2018 Ofir NachumShixiang GuHonglak LeeSergey Levine

Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of current HRL methods require careful task-specific design and on-policy training, making them difficult to apply in real-world scenarios... (read more)

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