GELATO: Geometrically Enriched Latent Model for Offline Reinforcement Learning

• URL: http://arxiv.org/abs/2102.11327v1
• Date: Mon, 22 Feb 2021 19:42:40 GMT
• Title: GELATO: Geometrically Enriched Latent Model for Offline Reinforcement Learning
• Authors: Guy Tennenholtz, Nir Baram, Shie Mannor
• Abstract summary: offline reinforcement learning approaches can be divided into proximal and uncertainty-aware methods. In this work, we demonstrate the benefit of combining the two in a latent variational model. Our proposed metrics measure both the quality of out of distribution samples as well as the discrepancy of examples in the data.
• Score: 54.291331971813364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Offline reinforcement learning approaches can generally be divided to proximal and uncertainty-aware methods. In this work, we demonstrate the benefit of combining the two in a latent variational model. We impose a latent representation of states and actions and leverage its intrinsic Riemannian geometry to measure distance of latent samples to the data. Our proposed metrics measure both the quality of out of distribution samples as well as the discrepancy of examples in the data. We integrate our metrics in a model-based offline optimization framework, in which proximity and uncertainty can be carefully controlled. We illustrate the geodesics on a simple grid-like environment, depicting its natural inherent topology. Finally, we analyze our approach and improve upon contemporary offline RL benchmarks.

Related papers

• A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models [6.647819824559201]
  We study the large-sample properties of a likelihood-based approach for estimating conditional deep generative models. Our results lead to the convergence rate of a sieve maximum likelihood estimator for estimating the conditional distribution.
  arXiv  Detail & Related papers  (2024-10-02T20:46:21Z)
• (Deep) Generative Geodesics [57.635187092922976]
  We introduce a newian metric to assess the similarity between any two data points. Our metric leads to the conceptual definition of generative distances and generative geodesics. Their approximations are proven to converge to their true values under mild conditions.
  arXiv  Detail & Related papers  (2024-07-15T21:14:02Z)
• SeMOPO: Learning High-quality Model and Policy from Low-quality Offline Visual Datasets [32.496818080222646]
  We propose a new approach to model-based offline reinforcement learning. We provide a theoretical guarantee of model uncertainty and performance bound of SeMOPO. Experimental results show that our method substantially outperforms all baseline methods.
  arXiv  Detail & Related papers  (2024-06-13T15:16:38Z)
• Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation [98.95383921866096]
  We study the problem of single-channel source separation (SCSS) We focus on cyclostationary signals, which are particularly suitable in a variety of application domains. We propose a deep learning approach using a U-Net architecture, which is competitive with the minimum MSE estimator.
  arXiv  Detail & Related papers  (2022-08-22T14:04:56Z)
• Data-heterogeneity-aware Mixing for Decentralized Learning [63.83913592085953]
  We characterize the dependence of convergence on the relationship between the mixing weights of the graph and the data heterogeneity across nodes. We propose a metric that quantifies the ability of a graph to mix the current gradients. Motivated by our analysis, we propose an approach that periodically and efficiently optimize the metric.
  arXiv  Detail & Related papers  (2022-04-13T15:54:35Z)
• A Model for Multi-View Residual Covariances based on Perspective Deformation [88.21738020902411]
  We derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups. We validate our model with synthetic and real data and integrate it into photometric and feature-based Bundle Adjustment.
  arXiv  Detail & Related papers  (2022-02-01T21:21:56Z)
• Latent Space Model for Higher-order Networks and Generalized Tensor Decomposition [18.07071669486882]
  We introduce a unified framework, formulated as general latent space models, to study complex higher-order network interactions. We formulate the relationship between the latent positions and the observed data via a generalized multilinear kernel as the link function. We demonstrate the effectiveness of our method on synthetic data.
  arXiv  Detail & Related papers  (2021-06-30T13:11:17Z)
• MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories [61.3299263929289]
  Simulation-based inference enables learning the parameters of a model even when its likelihood cannot be computed in practice. One class of methods uses data simulated with different parameters to infer an amortized estimator for the likelihood-to-evidence ratio. We show that this approach can be formulated in terms of mutual information between model parameters and simulated data.
  arXiv  Detail & Related papers  (2021-06-03T12:59:16Z)
• Identifying the latent space geometry of network models through analysis of curvature [7.644165047073435]
  We present a method to consistently estimate the manifold type, dimension, and curvature from an empirically relevant class of latent spaces. Our core insight comes by representing the graph as a noisy distance matrix based on the ties between cliques.
  arXiv  Detail & Related papers  (2020-12-19T00:35:29Z)
• Learning Flat Latent Manifolds with VAEs [16.725880610265378]
  We propose an extension to the framework of variational auto-encoders, where the Euclidean metric is a proxy for the similarity between data points. We replace the compact prior typically used in variational auto-encoders with a recently presented, more expressive hierarchical one. We evaluate our method on a range of data-sets, including a video-tracking benchmark.
  arXiv  Detail & Related papers  (2020-02-12T09:54:52Z)

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.