Results 171 to 180 of about 139,994 (273)

Optimized multi agent reinforcement learning algorithms with hybrid BiLSTM for cost efficient EV charging scheduling. [PDF]

Front Artif Intell
Khekare U, Vedaraj I S R.
europepmc   +1 more source

A review of the applications of multi-agent reinforcement learning in smart factories. [PDF]

Front Robot AI, 2022
Bahrpeyma F, Reichelt D.
europepmc   +1 more source

From Materials to Systems: Challenges and Solutions for Fast‐Charge/Discharge Na‐Ion Batteries

Advanced Energy Materials, EarlyView.
This review systematically analyzes the key characteristics limiting the fast‐charge/discharge capability of Na‐ion batteries (SIBs) from a multi‐scale perspective encompassing electrode materials, the electrode‐electrolyte interface, and the system. Furthermore, it presents practical solution strategies for the fundamental issues arising at each scale,
Bonyoung Ku, Sangbin Park, Sunha Hwang, Sunghyun Lim, Won‐Sub Yoon, Jongsoon Kim   +5 more
wiley   +1 more source

Flow control of three-dimensional cylinders transitioning to turbulence via multi-agent reinforcement learning. [PDF]

Commun Eng
Suárez P, Alcántara-Ávila F, Rabault J, Miró A, Font B, Lehmkuhl O, Vinuesa R.   +6 more
europepmc   +2 more sources

Multi-agent Reinforcement Learning for the Control of Three-Dimensional Rayleigh-Bénard Convection. [PDF]

Flow Turbul Combust
Vasanth J, Rabault J, Alcántara-Ávila F, Mortensen M, Vinuesa R.   +4 more
europepmc   +2 more sources

Multi-agent reinforcement learning driven resource game optimization for network slicing in MEC-enabled HetNets. [PDF]

Sci Rep
Mao K.
europepmc   +1 more source

Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents. [PDF]

Sensors (Basel), 2022
Kim M, Bae J, Wang B, Ko H, Lim JS.
europepmc   +1 more source

Safety of Sodium‐Ion Batteries: Evaluation and Perspective from Component Materials to Cells, Modules, and Packs

Advanced Energy Materials, EarlyView.
This review provides a bottom‐up evaluation of sodium‐ion battery safety, linking material degradation mechanisms, cell engineering parameters, and module/pack assembly. It emphasizes that understanding intrinsic material stability and establishing coordinated engineering control across hierarchical levels are vital for preventing degradation coupling ...
Won‐Gwang Lim, Seoa Kim, Erik A. Wu, Darren H. S. Tan, Shawn Peng, Xiaolin Li   +5 more
wiley   +1 more source

Multi-Agent Reinforcement Learning in Games: Research and Applications. [PDF]

Biomimetics (Basel)
Li H, Yang P, Liu W, Yan S, Zhang X, Zhu D.   +5 more
europepmc   +1 more source

Knowledge Reuse of Multi-Agent Reinforcement Learning in Cooperative Tasks. [PDF]

Entropy (Basel), 2022
Shi D, Tong J, Liu Y, Fan W.
europepmc   +1 more source