Risk-Aware Energy Scheduling for Edge Computing With Microgrid: A Multi-Agent Deep Reinforcement Learning Approach.
Md. Shirajum MunirSarder Fakhrul AbedinNguyen H. TranZhu HanEui-Nam HuhChoong Seon HongPublished in: IEEE Trans. Netw. Serv. Manag. (2021)
Keyphrases
- reinforcement learning
- multi agent
- scheduling problem
- scheduling algorithm
- function approximation
- edge detection
- markov decision processes
- learning agents
- edge information
- state space
- energy minimization
- multi agent reinforcement learning
- multi agent environments
- cooperative
- agent oriented
- single agent
- round robin
- reinforcement learning agents
- learning algorithm
- risk management
- resource allocation
- energy consumption
- intelligent agents
- multi agent systems
- risk assessment
- model free
- transfer learning
- multiagent systems
- risk measures
- decision making