10 citations · 18 across the 3 of their papers we have counts for
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cs.AI2021
Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning
Hankz Hankui Zhuo, Shuting Deng, Mu Jin +4
Despite of achieving great success in real-world applications, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, i.e., data efficiency, lack of the i…
cs.AI2021★ 10 cited
The Powerful Use of AI in the Energy Sector: Intelligent Forecasting
Erik Blasch, Haoran Li, Zhihao Ma +1
Artificial Intelligence (AI) techniques continue to broaden across governmental and public sectors, such as power and energy - which serve as critical infrastructures for most soci…
cs.AI2021★ 8 cited
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning
Zhihao Ma, Yuzheng Zhuang, Paul Weng +4
Recent progress in deep reinforcement learning (DRL) can be largely attributed to the use of neural networks. However, this black-box approach fails to explain the learned policy i…