Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 2192-113X

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning ; volume:10 ; number:1 ; day:9 ; month:9 ; year:2021 ; pages:1-13 ; date:12.2021
Journal of Cloud Computing ; 10, Heft 1 (9.9.2021), 1-13, 12.2021

Creator: Pu, Tianjiao
Wang, Xinying
Cao, Yifan
Liu, Zhicheng
Qiu, Chao
Qiao, Ji
Zhang, Shuhua

Contributor: SpringerLink (Online service)

DOI: 10.1186/s13677-021-00259-1

URN: urn:nbn:de:101:1-2021112318454535108228

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:27 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Pu, Tianjiao
Wang, Xinying
Cao, Yifan
Liu, Zhicheng
Qiu, Chao
Qiao, Ji
Zhang, Shuhua
SpringerLink (Online service)

Other Objects (12)

Hochschulschrift

Multi-agent reinforcement learning algorithms

Hochschulschrift

Multi-agent reinforcement learning algorithms

Scheduling in Multiagent Systems Using Reinforcement Learning

Multi-agent reinforcement learning for character control

Deep multiagent reinforcement learning: challenges and directions

Communication approaches in Multi-Agent Reinforcement Learning

Multi-agent deep reinforcement learning: a survey

Centralized multi-agent implementation for securing critical loads in PV based microgrid

Multi-Agent Reinforcement Learning for Interactive Decision-Making

Multi-Agent Natural Actor-Critic Reinforcement Learning Algorithms

Emergence and resilience in multi-agent reinforcement learning

Neural scaling laws for multi-agent reinforcement learning

Hochschulschrift

Multi-agent reinforcement learning algorithms

Hochschulschrift

Multi-agent reinforcement learning algorithms

Scheduling in Multiagent Systems Using Reinforcement Learning

Multi-agent reinforcement learning for character control

Deep multiagent reinforcement learning: challenges and directions

Communication approaches in Multi-Agent Reinforcement Learning

Multi-agent deep reinforcement learning: a survey

Centralized multi-agent implementation for securing critical loads in PV based microgrid

Multi-Agent Reinforcement Learning for Interactive Decision-Making

Multi-Agent Natural Actor-Critic Reinforcement Learning Algorithms

Emergence and resilience in multi-agent reinforcement learning

Neural scaling laws for multi-agent reinforcement learning

Hochschulschrift

Multi-agent reinforcement learning algorithms

Hochschulschrift

Multi-agent reinforcement learning algorithms

Scheduling in Multiagent Systems Using Reinforcement Learning

Multi-agent reinforcement learning for character control

Deep multiagent reinforcement learning: challenges and directions

Communication approaches in Multi-Agent Reinforcement Learning

Multi-agent deep reinforcement learning: a survey

Centralized multi-agent implementation for securing critical loads in PV based microgrid

Multi-Agent Reinforcement Learning for Interactive Decision-Making

Multi-Agent Natural Actor-Critic Reinforcement Learning Algorithms

Emergence and resilience in multi-agent reinforcement learning

Neural scaling laws for multi-agent reinforcement learning

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Multi-agent reinforcement learning algorithms

Multi-agent reinforcement learning algorithms

Scheduling in Multiagent Systems Using Reinforcement Learning

Multi-agent reinforcement learning for character control

Deep multiagent reinforcement learning: challenges and directions

Communication approaches in Multi-Agent Reinforcement Learning

Multi-agent deep reinforcement learning: a survey

Centralized multi-agent implementation for securing critical loads in PV based microgrid

Multi-Agent Reinforcement Learning for Interactive Decision-Making

Multi-Agent Natural Actor-Critic Reinforcement Learning Algorithms

Emergence and resilience in multi-agent reinforcement learning

Neural scaling laws for multi-agent reinforcement learning

Multi-agent reinforcement learning algorithms

Multi-agent reinforcement learning algorithms

Scheduling in Multiagent Systems Using Reinforcement Learning

Multi-agent reinforcement learning for character control

Deep multiagent reinforcement learning: challenges and directions

Communication approaches in Multi-Agent Reinforcement Learning

Multi-agent deep reinforcement learning: a survey

Centralized multi-agent implementation for securing critical loads in PV based microgrid

Multi-Agent Reinforcement Learning for Interactive Decision-Making

Multi-Agent Natural Actor-Critic Reinforcement Learning Algorithms

Emergence and resilience in multi-agent reinforcement learning

Neural scaling laws for multi-agent reinforcement learning

Multi-agent reinforcement learning algorithms

Multi-agent reinforcement learning algorithms

Scheduling in Multiagent Systems Using Reinforcement Learning

Multi-agent reinforcement learning for character control

Deep multiagent reinforcement learning: challenges and directions

Communication approaches in Multi-Agent Reinforcement Learning

Multi-agent deep reinforcement learning: a survey

Centralized multi-agent implementation for securing critical loads in PV based microgrid

Multi-Agent Reinforcement Learning for Interactive Decision-Making

Multi-Agent Natural Actor-Critic Reinforcement Learning Algorithms

Emergence and resilience in multi-agent reinforcement learning

Neural scaling laws for multi-agent reinforcement learning

Related objects

Reset password