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Reinforcement Learning Enhanced Explainer for Graph Neural Networks Caihua Shan1, Yifei Shen2, Yao Zhang3, Xiang Li4, Dongsheng Li1 1Microsoft Research Asia {caihua.shan,dongsheng.li}@microsoft.com 2The Hong Kong University of Science and Technology yshenaw@connect.ust.hk

Oct 16, 2019 · Recent advances in Deep Reinforcement Learning (DRL) have shown a significant improvement in decision-making problems. The networking community has started to investigate how DRL can provide a new breed of solutions to relevant optimization problems, such as routing. However, most of the state-of-the-art DRL-based networking techniques fail to generalize, this means that they can only operate ...

01.07.2021 · %0 Conference Paper %T Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks %A Eli Meirom %A Haggai Maron %A Shie Mannor %A Gal Chechik %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139 …

Graph neural networks (GNNs) have recently emerged as revolutionary technologies for machine learning tasks on graphs. In GNNs, the graph ...

Graph neural networks (GNNs) and reinforcement learning (RL) have been a great success in solving many complicated real-world problems. In this talk, we will demonstrate their applications in a long-standing and essential area, electronic design automation (EDA).

3.11 Graph Classification, 3.12 Reinforcement Learning ... Relational Inductive Biases, Deep Learning, and Graph Networks. arxiv 2018. paper.

Abstract: Graph neural networks (GNNs) have recently emerged as revolutionary technologies for machine learning tasks on graphs. In GNNs, the graph ...

here is a paper that combines actor-critic reinforcement learning and graph neural networks: Paper: https://arxiv.org/abs/2006.12576.

16.10.2019 · Recent advances in Deep Reinforcement Learning (DRL) have shown a significant improvement in decision-making problems. The networking community has started to investigate how DRL can provide a new breed of solutions to relevant optimization problems, such as routing. However, most of the state-of-the-art DRL-based networking techniques fail to generalize, this …

08.10.2020 · Reinforcement Learning with Neural Networks While it’s manageable to create and use a q-table for simple environments, it’s quite difficult with some real-life environments. The number of actions and states in a real-life environment can be thousands, making it extremely inefficient to manage q-values in a table .

Reinforcement Learning and Graph Neural Networks. Eli A. Meirom 1 Haggai Maron 1 Shie Mannor 1 Gal Chechik 1. Abstract.

10.05.2021 · Learning to Play Football with Graphs and Reinforcement Learning. In this article we’re going to find out how you can train your very own virtual football team by combining the concept of reinforcement learning with that of graph neural networks - and whether you should.

Oct 16, 2019 · Recent advances in Deep Reinforcement Learning (DRL) have shown a significant improvement in decision-making problems. The networking community has started to investigate how DRL can provide a new breed of solutions to relevant optimization problems, such as routing. However, most of the state-of-the-art DRL-based networking techniques fail to generalize, this means that they can only operate ...

Reinforcement Learning Enhanced Explainer for Graph Neural Networks Caihua Shan1, Yifei Shen2, Yao Zhang3, Xiang Li4, Dongsheng Li1 1Microsoft Research Asia {caihua.shan,dongsheng.li}@microsoft.com 2The Hong Kong University of Science and Technology yshenaw@connect.ust.hk 3Fudan University yaozhang@fudan.edu.cn 4East China Normal University

16.10.2019 · Deep Reinforcement Learning meets Graph Neural Networks: ... The reason behind this important limitation is that existing DRL networking solutions use standard neural networks (e.g., fully connected), which are unable to learn graph-structured information.

May 10, 2021 · Learning to Play Football with Graphs and Reinforcement Learning. In this article we’re going to find out how you can train your very own virtual football team by combining the concept of reinforcement learning with that of graph neural networks - and whether you should.

Sep 13, 2020 · 3. Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in Cognitive Radio Network. In this section, the details of the spectrum-efficiency resource allocation algorithm for an underlay CRN is provided, which is a DRL approach with GCN.

In this paper, we propose a deep reinforcement learning framework to control the rebalancing of AMoD systems through graph neural networks.