Large-Scale Machine Learning Cluster Scheduling via Multi-Agent Graph Reinforcement Learning.
Xiaoyang ZhaoChuan WuPublished in: IEEE Trans. Netw. Serv. Manag. (2022)
Keyphrases
- reinforcement learning
- multi agent
- machine learning
- learning algorithm
- state space
- function approximation
- graph theory
- supervised learning
- small scale
- random walk
- multi agent environments
- graph representation
- markov decision processes
- multi agent systems
- reinforcement learning algorithms
- multi agent reinforcement learning
- graph structure
- proximity graph
- learning process
- learning problems
- real world
- scheduling algorithm
- massive graphs
- autonomous agents
- scheduling problem
- learning agents
- weighted graph
- graph mining
- data clustering
- normalized cut
- cooperative
- machine learning algorithms
- clustering algorithm
- partially observable markov decision processes
- natural language processing
- model free
- decision trees
- reinforcement learning agents
- graph partitioning
- graph model
- knowledge acquisition
- optimal policy
- structured data
- multiagent systems
- machine learning methods
- nodes of a graph
- feature selection
- partial observability
- pattern recognition
- graph kernels
- temporal difference
- markov decision process
- similarity matrix
- single agent
- dynamic programming
- learning tasks
- text mining
- resource constraints
- learning classifier systems
- directed graph
- spectral clustering