Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning

• URL: http://arxiv.org/abs/2102.04220v1
• Date: Mon, 8 Feb 2021 14:15:13 GMT
• Title: Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement Learning
• Authors: Zhengyao Jiang, Pasquale Minervini, Minqi Jiang, Tim Rocktaschel
• Abstract summary: We show that we can incorporate relational inductive biases, encoded in the form of relational graphs, into agents. We propose Grid-to-Graph (GTG), a mapping from grid structures to relational graphs that carry useful inductive biases. We show that GTG produces agents that can jointly reason over observations and environment encoded dynamics in knowledge bases.
• Score: 8.169818701603313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although reinforcement learning has been successfully applied in many domains in recent years, we still lack agents that can systematically generalize. While relational inductive biases that fit a task can improve generalization of RL agents, these biases are commonly hard-coded directly in the agent's neural architecture. In this work, we show that we can incorporate relational inductive biases, encoded in the form of relational graphs, into agents. Based on this insight, we propose Grid-to-Graph (GTG), a mapping from grid structures to relational graphs that carry useful spatial relational inductive biases when processed through a Relational Graph Convolution Network (R-GCN). We show that, with GTG, R-GCNs generalize better both in terms of in-distribution and out-of-distribution compared to baselines based on Convolutional Neural Networks and Neural Logic Machines on challenging procedurally generated environments and MinAtar. Furthermore, we show that GTG produces agents that can jointly reason over observations and environment dynamics encoded in knowledge bases.

Related papers

• Understanding the Effect of GCN Convolutions in Regression Tasks [8.299692647308323]
  Graph Convolutional Networks (GCNs) have become a pivotal method in machine learning for modeling functions over graphs. This paper provides a formal analysis of the impact of convolution operators on regression tasks over homophilic networks.
  arXiv  Detail & Related papers  (2024-10-26T04:19:52Z)
• A Manifold Perspective on the Statistical Generalization of Graph Neural Networks [84.01980526069075]
  We take a manifold perspective to establish the statistical generalization theory of GNNs on graphs sampled from a manifold in the spectral domain. We prove that the generalization bounds of GNNs decrease linearly with the size of the graphs in the logarithmic scale, and increase linearly with the spectral continuity constants of the filter functions.
  arXiv  Detail & Related papers  (2024-06-07T19:25:02Z)
• Advective Diffusion Transformers for Topological Generalization in Graph Learning [69.2894350228753]
  We show how graph diffusion equations extrapolate and generalize in the presence of varying graph topologies. We propose a novel graph encoder backbone, Advective Diffusion Transformer (ADiT), inspired by advective graph diffusion equations.
  arXiv  Detail & Related papers  (2023-10-10T08:40:47Z)
• Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework [12.116373546916078]
  Graph convolutional networks (GCNs) have been attracting widespread attentions due to their encouraging performance and powerful generalizations. In this paper, we induce an interpretable regularizer-centerd optimization framework, in which by building appropriate regularizers we can interpret most GCNs. Under the proposed framework, we devise a dual-regularizer graph convolutional network (dubbed tsGCN) to capture topological and semantic structures from graph data.
  arXiv  Detail & Related papers  (2023-01-11T05:51:33Z)
• Stable and Transferable Hyper-Graph Neural Networks [95.07035704188984]
  We introduce an architecture for processing signals supported on hypergraphs via graph neural networks (GNNs) We provide a framework for bounding the stability and transferability error of GNNs across arbitrary graphs via spectral similarity.
  arXiv  Detail & Related papers  (2022-11-11T23:44:20Z)
• Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph [58.99478502486377]
  We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
  arXiv  Detail & Related papers  (2022-09-23T05:24:18Z)
• Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI [24.914412344973996]
  We generalize graph convolutional neural networks (GCN) to the complex domain. We prove that complex-valued GCNs are stable with respect to perturbations of the underlying graph support. We apply complex GCN to power grid state forecasting, power grid-attack detection and localization.
  arXiv  Detail & Related papers  (2022-08-17T18:56:48Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition [14.924672048447338]
  We introduce a novel framework for graph convolutional networks that learns the topological properties of graphs. The design principle of our method is based on the optimization of a constrained objective function. Experiments conducted on the challenging task of skeleton-based action recognition shows the superiority of the proposed method.
  arXiv  Detail & Related papers  (2021-12-06T19:43:26Z)
• Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes [144.6048446370369]
  Graph convolutional neural networks(GCNs) have recently demonstrated promising results on graph-based semi-supervised classification. We propose a GP regression model via GCNs(GPGC) for graph-based semi-supervised learning. We conduct extensive experiments to evaluate GPGC and demonstrate that it outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-26T10:02:32Z)

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.