Articles

Learning new attack vectors from misuse cases with deep reinforcement learning

in Smart Grids

• Eric M. S. P. Veith
• Arlena Wellßow
• Mathias Uslar

Optimal scheduling of microgrids considering real power losses of grid-connected microgrid systems

in Smart Grids

• Shengbo Sun
• Wei Guo
• Qiwei Wang
• Peng Tao
• Gang Li
• Zhenbing Zhao

Deep Reinforcement Learning for Constrained Field Development Optimization in Subsurface Two-phase Flow

in Optimization

• Yusuf Nasir
• Jincong He
• Chaoshun Hu
• Shusei Tanaka
• Kainan Wang
• XianHuan Wen

Toward robust and scalable deep spiking reinforcement learning

• Mahmoud Akl
• Deniz Ergene
• Florian Walter
• Alois Knoll

Perspective Taking in Deep Reinforcement Learning Agents

• Aqeel Labash
• Jaan Aru
• Tambet Matiisen
• Ardi Tampuu
• Raul Vicente

Learning to play against any mixture of opponents

in Machine Learning and Artificial Intelligence

• Max Olan Smith
• Thomas Anthony
• Michael P. Wellman

Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems

in Recommender Systems

• Siyu Wang
• Yuanjiang Cao
• Xiaocong Chen
• Lina Yao
• Xianzhi Wang
• Quan Z. Sheng

Moving the needle: Employing deep reinforcement learning to push the boundaries of coarse-grained vaccine models

in Systems Immunology

• Jonathan G. Faris
• Daniel Orbidan
• Charles Wells
• Brenden K. Petersen
• Kayla G. Sprenger

CEA-FJSP: Carbon emission-aware flexible job-shop scheduling based on deep reinforcement learning

in Environmental Informatics and Remote Sensing

• Shiyong Wang
• Jiaxian Li
• Hao Tang
• Juan Wang