Jingyu Cao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jingyu Cao has a total of 11 co-authors. Here are the top co-authors by number of shared publications:
- Changyin Sun
Southeast University, Nanjing, China
5 shared publications - Lu Dong
School of Cyber Science and Engineering, Southeast University, Nanjing, China
4 shared publications - Yuanda Wang
School of Automation, Southeast University, Nanjing, China
2 shared publications - Chaoxu Mu
Tianjin University
1 shared publication - Guangran Cheng
School of Automation, Southeast University, Nanjing, China
1 shared publication - Jiawei Wang
College of Electronics and Information Engineering Tongji University 201804 Shanghai China
1 shared publication
Latest Publications
- Open Access
XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
In this paper, we present XuanCe, a comprehensive and unified deep reinforcement learning (DRL) library designed to be compatible with PyTorch, TensorFlow, and MindSpore. XuanCe offers a wide range of functionalities, including over 40 classical DRL and multi-agent DRL algorithms, with the flexibility to easily incorporate new algorithms and environments. It is a versatile DRL library that supports CPU, GPU, and Ascend, and can be executed on various operating systems such as Ubuntu, Windows, MacOS, and EulerOS. Extensive benchmarks conducted on popular environments including MuJoCo, Atari, and StarCraftII multi-agent challenge demonstrate the library's impressive performance. XuanCe is open-source and can be accessed at https://github.com/agi-brain/xuance.git.
Open AccessXuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
In this paper, we present XuanCe, a comprehensive and unified deep reinforcement learning (DRL) library designed to be compatible with PyTorch, TensorFlow, and MindSpore. XuanCe offers a wide range of functionalities, including over 40 classical DRL and multi-agent DRL algorithms, with the flexibility to easily incorporate new algorithms and environments. It is a versatile DRL library that supports CPU, GPU, and Ascend, and can be executed on various operating systems such as Ubuntu, Windows, MacOS, and EulerOS. Extensive benchmarks conducted on popular environments including MuJoCo, Atari, and StarCraftII multi-agent challenge demonstrate the library's impressive performance. XuanCe is open-source and can be accessed at https://github.com/agi-brain/xuance.git.
- Open Access
- 13 December 2023
- Published by Springer Nature in Neural Computing and Applications
- Vol. 36 (10) , 5647
Correction: Hierarchical reinforcement learning for kinematic control tasks with parameterized action spaces
- 13 December 2023
- Published by Springer Nature in Neural Computing and Applications
- Vol. 36 (10) , 5647
Open AccessCorrection: Hierarchical reinforcement learning for kinematic control tasks with parameterized action spaces
- 31 October 2023
- Published by Springer Nature in Neural Computing and Applications
- Vol. 36 (1) , 323-336
Hierarchical reinforcement learning for kinematic control tasks with parameterized action spaces
- 31 October 2023
- Published by Springer Nature in Neural Computing and Applications
- Vol. 36 (1) , 323-336
Hierarchical reinforcement learning for kinematic control tasks with parameterized action spaces
- 10 October 2023
- Published by Springer Nature in Neural Computing and Applications
- Vol. 36 (1) , 273-287
Hierarchical multi-agent reinforcement learning for cooperative tasks with sparse rewards in continuous domain
- 10 October 2023
- Published by Springer Nature in Neural Computing and Applications
- Vol. 36 (1) , 273-287
Hierarchical multi-agent reinforcement learning for cooperative tasks with sparse rewards in continuous domain
- 1 June 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Journal of Systems Engineering and Electronics
- Vol. 33 (3) , 693-705
Day-ahead scheduling based on reinforcement learning with hybrid action space
Driven by the improvement of the smart grid, the active distribution network (ADN) has attracted much attention due to its characteristic of active management. By making full use of electricity price signals for optimal scheduling, the total cost of the ADN can be reduced. However, the optimal day-ahead scheduling problem is challenging since the future electricity price is unknown. Moreover, in ADN, some schedulable variables are continuous while some schedulable variables are discrete, which increases the difficulty of determining the optimal scheduling scheme. In this paper, the day-ahead scheduling problem of the ADN is formulated as a Markov decision process (MDP) with continuous-discrete hybrid action space. Then, an algorithm based on multi-agent hybrid reinforcement learning (HRL) is proposed to obtain the optimal scheduling scheme. The proposed algorithm adopts the structure of centralized training and decentralized execution, and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables. The simulation experiment results demonstrate the effectiveness of the algorithm.
- 1 June 2022
- Published by Institute of Electrical and Electronics Engineers (IEEE) in Journal of Systems Engineering and Electronics
- Vol. 33 (3) , 693-705
Day-ahead scheduling based on reinforcement learning with hybrid action space
Driven by the improvement of the smart grid, the active distribution network (ADN) has attracted much attention due to its characteristic of active management. By making full use of electricity price signals for optimal scheduling, the total cost of the ADN can be reduced. However, the optimal day-ahead scheduling problem is challenging since the future electricity price is unknown. Moreover, in ADN, some schedulable variables are continuous while some schedulable variables are discrete, which increases the difficulty of determining the optimal scheduling scheme. In this paper, the day-ahead scheduling problem of the ADN is formulated as a Markov decision process (MDP) with continuous-discrete hybrid action space. Then, an algorithm based on multi-agent hybrid reinforcement learning (HRL) is proposed to obtain the optimal scheduling scheme. The proposed algorithm adopts the structure of centralized training and decentralized execution, and different methods are applied to determine the selection policy of continuous scheduling variables and discrete scheduling variables. The simulation experiment results demonstrate the effectiveness of the algorithm.