Energy-Saving Train Operation Synergy Based on Multi-Agent Deep Reinforcement Learning on Spark Cloud.
Mengying ShangYonghua ZhouYiduo MeiJie ZhaoHamido FujitaPublished in: IEEE Trans. Veh. Technol. (2023)
Keyphrases
- energy saving
- reinforcement learning
- multi agent
- air conditioning
- energy consumption
- power consumption
- high speed railway
- wireless sensor networks
- power management
- energy efficiency
- energy conservation
- power saving
- cloud computing
- global warming
- data center
- function approximation
- save energy
- energy efficient
- multi agent environments
- energy management
- multi agent systems
- learning agents
- state space
- optimal policy
- real time
- machine learning
- single agent
- reinforcement learning algorithms
- electricity consumption
- routing protocol
- solar energy
- learning algorithm
- total energy
- network topology
- computational intelligence
- sensor nodes