Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning

no code implementations • 18 Dec 2021 • Hankz Hankui Zhuo, Shuting Deng, Mu Jin, Zhihao Ma, Kebing Jin, Chen Chen, Chao Yu

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 interpretability and transferability.

Montezuma's Revenge reinforcement-learning +1

The Powerful Use of AI in the Energy Sector: Intelligent Forecasting

no code implementations • 3 Nov 2021 • Erik Blasch, Haoran Li, Zhihao Ma, Yang Weng

To meet society requirements, this paper proposes a methodology to develop, deploy, and evaluate AI systems in the energy sector by: (1) understanding the power system measurements with physics, (2) designing AI algorithms to forecast the need, (3) developing robust and accountable AI methods, and (4) creating reliable measures to evaluate the performance of the AI model.

Dimensionality Reduction

Learning Symbolic Rules for Interpretable Deep Reinforcement Learning

no code implementations • 15 Mar 2021 • Zhihao Ma, Yuzheng Zhuang, Paul Weng, Hankz Hankui Zhuo, Dong Li, Wulong Liu, Jianye Hao

To address this challenge and improve the transparency, we propose a Neural Symbolic Reinforcement Learning framework by introducing symbolic logic into DRL.

reinforcement-learning Reinforcement Learning (RL)

Interpretable Reinforcement Learning With Neural Symbolic Logic

no code implementations • 1 Jan 2021 • Zhihao Ma, Yuzheng Zhuang, Paul Weng, Dong Li, Kun Shao, Wulong Liu, Hankz Hankui Zhuo, Jianye Hao

Recent progress in deep reinforcement learning (DRL) can be largely attributed to the use of neural networks.

Hierarchical Reinforcement Learning reinforcement-learning +2