Graph neural induction of value iteration

• URL: http://arxiv.org/abs/2009.12604v1
• Date: Sat, 26 Sep 2020 14:09:16 GMT
• Title: Graph neural induction of value iteration
• Authors: Andreea Deac, Pierre-Luc Bacon, Jian Tang
• Abstract summary: We propose a graph neural network (GNN) that executes the value iteration (VI) algorithm, across arbitrary environment models, with direct supervision on the intermediate steps of VI. The results indicate that GNNs are able to model value iteration accurately, recovering favourable metrics and policies across a variety of out-of-distribution tests.
• Score: 22.582832003418826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many reinforcement learning tasks can benefit from explicit planning based on an internal model of the environment. Previously, such planning components have been incorporated through a neural network that partially aligns with the computational graph of value iteration. Such network have so far been focused on restrictive environments (e.g. grid-worlds), and modelled the planning procedure only indirectly. We relax these constraints, proposing a graph neural network (GNN) that executes the value iteration (VI) algorithm, across arbitrary environment models, with direct supervision on the intermediate steps of VI. The results indicate that GNNs are able to model value iteration accurately, recovering favourable metrics and policies across a variety of out-of-distribution tests. This suggests that GNN executors with strong supervision are a viable component within deep reinforcement learning systems.

Related papers

• Spatiotemporal Learning on Cell-embedded Graphs [6.8090864965073274]
  We introduce a learnable cell attribution to the node-edge message passing process, which better captures the spatial dependency of regional features. Experiments on various PDE systems and one real-world dataset demonstrate that CeGNN achieves superior performance compared with other baseline models.
  arXiv  Detail & Related papers  (2024-09-26T16:22:08Z)
• DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment [57.62885438406724]
  Graph neural networks are recognized for their strong performance across various applications. BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. We propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning.
  arXiv  Detail & Related papers  (2024-06-04T07:24:51Z)
• Attentional Graph Neural Networks for Robust Massive Network Localization [20.416879207269446]
  Graph neural networks (GNNs) have emerged as a prominent tool for classification tasks in machine learning. This paper integrates GNNs with attention mechanism to tackle a challenging nonlinear regression problem: network localization. We first introduce a novel network localization method based on graph convolutional network (GCN), which exhibits exceptional precision even under severe non-line-of-sight (NLOS) conditions.
  arXiv  Detail & Related papers  (2023-11-28T15:05:13Z)
• Continuous Neural Algorithmic Planners [3.9715120586766584]
  XLVIN is a graph neural network that simulates the value algorithm in deep reinforcement learning agents. It allows model-free iteration planning without access to privileged information about the environment. We show how neural algorithmic reasoning can make a measurable impact in higher-dimensional continuous control settings.
  arXiv  Detail & Related papers  (2022-11-29T00:19:35Z)
• Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks [55.98291376393561]
  Graph neural networks (GNNs) have emerged as a powerful tool for learning software engineering tasks. Recurrent neural networks (RNNs) are well-suited to long sequential chains of reasoning, but they do not naturally incorporate program structure. We introduce a novel GNN architecture, the Instruction Pointer Attention Graph Neural Networks (IPA-GNN), which improves systematic generalization on the task of learning to execute programs.
  arXiv  Detail & Related papers  (2020-10-23T19:12:30Z)
• Implicit Graph Neural Networks [46.0589136729616]
  We propose a graph learning framework called Implicit Graph Neural Networks (IGNN) IGNNs consistently capture long-range dependencies and outperform state-of-the-art GNN models.
  arXiv  Detail & Related papers  (2020-09-14T06:04:55Z)
• Modeling from Features: a Mean-field Framework for Over-parameterized Deep Neural Networks [54.27962244835622]
  This paper proposes a new mean-field framework for over- parameterized deep neural networks (DNNs) In this framework, a DNN is represented by probability measures and functions over its features in the continuous limit. We illustrate the framework via the standard DNN and the Residual Network (Res-Net) architectures.
  arXiv  Detail & Related papers  (2020-07-03T01:37:16Z)
• Stochastic Graph Neural Networks [123.39024384275054]
  Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning. Current GNN architectures assume ideal scenarios and ignore link fluctuations that occur due to environment, human factors, or external attacks. In these situations, the GNN fails to address its distributed task if the topological randomness is not considered accordingly.
  arXiv  Detail & Related papers  (2020-06-04T08:00:00Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)
• EdgeNets:Edge Varying Graph Neural Networks [179.99395949679547]
  This paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors. This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs)
  arXiv  Detail & Related papers  (2020-01-21T15:51:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.