Foresight of Graph Reinforcement Learning Latent Permutations Learnt by Gumbel Sinkhorn Network.
Tianqi ShenHong ZhangDing YuanJiaping XiaoYifan YangPublished in: CoRR (2021)
Keyphrases
- reinforcement learning
- fully connected
- network model
- computer networks
- graph representation
- function approximation
- network structure
- dynamic networks
- complex networks
- path length
- graph theory
- random walk
- random graphs
- wireless sensor networks
- graphical representation
- spanning tree
- graph model
- latent variables
- graph mining
- graph databases
- optimal control
- graph structure
- temporal difference
- link prediction
- edge weights
- directed graph
- protein interaction networks
- connected components
- state space
- clustering coefficient