Large-scale multi-agent reinforcement learning-based method for coordinated output voltage control of solid oxide fuel cell

Title
Large-scale multi-agent reinforcement learning-based method for coordinated output voltage control of solid oxide fuel cell
Authors
Keywords
Large-scale deep reinforcement learning, Pygmalion effect-based multi-agent double delay deep deterministic policy gradient algorithm (PEB-MA4DPG), DC/DC converter, Fuel reformer, Output voltage coordinated control, Solid oxide fuel cell (SOFC)
Journal
Case Studies in Thermal Engineering
Volume 30, Issue -, Pages 101752
Publisher
Elsevier BV
Online
2022-01-04
DOI
10.1016/j.csite.2021.101752

Ask authors/readers for more resources

Reprint

Contact the author

Publish scientific posters with Peeref

Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.

Learn More

Find the ideal target journal for your manuscript

Explore over 38,000 international journals covering a vast array of academic fields.

Search