Learning to Interrupt: A Hierarchical Deep Reinforcement Learning Framework for Efficient Exploration

30 Jul 2018Tingguang LiJin PanDelong ZhuMax Q. -H. Meng

To achieve scenario intelligence, humans must transfer knowledge to robots by developing goal-oriented algorithms, which are sometimes insensitive to dynamically changing environments. While deep reinforcement learning achieves significant success recently, it is still extremely difficult to be deployed in real robots directly... (read more)

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