Digital Twin-Driven Collaborative Scheduling for Heterogeneous Task and Edge-End Resource via Multi-Agent Deep Reinforcement Learning.
Chi XuZixuan TangHaibin YuPeng ZengLinghe KongPublished in: IEEE J. Sel. Areas Commun. (2023)
Keyphrases
- reinforcement learning
- multi agent
- grid environment
- resource allocation
- resource constraints
- cooperative
- resource consumption
- scheduling algorithm
- function approximation
- learning algorithm
- scheduling problem
- reinforcement learning algorithms
- web resources
- multi agent reinforcement learning
- intelligent agents
- resource requirements
- edge detection
- learning agents
- multi agent systems
- collaborative learning
- state space
- edge information
- dynamic programming
- scarce resources
- reinforcement learning agents
- grid resource
- image processing
- model free
- multiple agents
- coalition formation
- grid computing
- multiagent systems
- data driven
- mobile robot
- action selection
- single agent
- resource management
- edge detector
- team formation
- multiagent reinforcement learning
- learning process