PDSketch: Integrated Planning Domain Programming and Learning

• URL: http://arxiv.org/abs/2303.05501v2
• Date: Thu, 24 Aug 2023 17:48:05 GMT
• Title: PDSketch: Integrated Planning Domain Programming and Learning
• Authors: Jiayuan Mao, Tom\'as Lozano-P\'erez, Joshua B. Tenenbaum, Leslie Pack Kaelbling
• Abstract summary: We present a new domain definition language, named PDSketch. It allows users to flexibly define high-level structures in the transition models. Details of the transition model will be filled in by trainable neural networks.
• Score: 86.07442931141637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies a model learning and online planning approach towards building flexible and general robots. Specifically, we investigate how to exploit the locality and sparsity structures in the underlying environmental transition model to improve model generalization, data-efficiency, and runtime-efficiency. We present a new domain definition language, named PDSketch. It allows users to flexibly define high-level structures in the transition models, such as object and feature dependencies, in a way similar to how programmers use TensorFlow or PyTorch to specify kernel sizes and hidden dimensions of a convolutional neural network. The details of the transition model will be filled in by trainable neural networks. Based on the defined structures and learned parameters, PDSketch automatically generates domain-independent planning heuristics without additional training. The derived heuristics accelerate the performance-time planning for novel goals.

Related papers

• A New View on Planning in Online Reinforcement Learning [19.35031543927374]
  This paper investigates a new approach to model-based reinforcement learning using background planning. We show that our GSP algorithm can propagate value from an abstract space in a manner that helps a variety of base learners learn significantly faster in different domains.
  arXiv  Detail & Related papers  (2024-06-03T17:45:19Z)
• Structural Pruning of Pre-trained Language Models via Neural Architecture Search [7.833790713816726]
  Pre-trained language models (PLM) mark the state-of-the-art for natural language understanding task when fine-tuned on labeled data. This paper explores neural architecture search (NAS) for structural pruning to find sub-parts of the fine-tuned network that optimally trade-off efficiency.
  arXiv  Detail & Related papers  (2024-05-03T17:34:57Z)
• Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture [68.13678918660872]
  We design a more capable parameter-sharing architecture based on matrix product operator (MPO) MPO decomposition can reorganize and factorize the information of a parameter matrix into two parts. Our architecture shares the central tensor across all layers for reducing the model size.
  arXiv  Detail & Related papers  (2023-03-27T02:34:09Z)
• Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
  We construct a dataset of neural network checkpoints and train a generative model on the parameters. We find that our approach successfully generates parameters for a wide range of loss prompts. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
  arXiv  Detail & Related papers  (2022-09-26T17:59:58Z)
• Goal-Space Planning with Subgoal Models [18.43265820052893]
  This paper investigates a new approach to model-based reinforcement learning using background planning. We show that our GSP algorithm can propagate value from an abstract space in a manner that helps a variety of base learners learn significantly faster in different domains.
  arXiv  Detail & Related papers  (2022-06-06T20:59:07Z)
• Learning Features with Parameter-Free Layers [22.92568642331809]
  This paper argues that simple built-in parameter-free operations can be a favorable alternative to the efficient trainable layers in a network architecture. Experiments on the ImageNet dataset demonstrate that the network architectures with parameter-free operations could enjoy the advantages of further efficiency in terms of model speed, the number of the parameters, and FLOPs.
  arXiv  Detail & Related papers  (2022-02-06T14:03:36Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Visual Learning-based Planning for Continuous High-Dimensional POMDPs [81.16442127503517]
  Visual Tree Search (VTS) is a learning and planning procedure that combines generative models learned offline with online model-based POMDP planning. VTS bridges offline model training and online planning by utilizing a set of deep generative observation models to predict and evaluate the likelihood of image observations in a Monte Carlo tree search planner. We show that VTS is robust to different observation noises and, since it utilizes online, model-based planning, can adapt to different reward structures without the need to re-train.
  arXiv  Detail & Related papers  (2021-12-17T11:53:31Z)
• Deep Parametric Continuous Convolutional Neural Networks [92.87547731907176]
  Parametric Continuous Convolution is a new learnable operator that operates over non-grid structured data. Our experiments show significant improvement over the state-of-the-art in point cloud segmentation of indoor and outdoor scenes.
  arXiv  Detail & Related papers  (2021-01-17T18:28:23Z)
• EPNE: Evolutionary Pattern Preserving Network Embedding [26.06068388979255]
  We propose EPNE, a temporal network embedding model preserving evolutionary patterns of the local structure of nodes. With the adequate modeling of temporal information, our model is able to outperform other competitive methods in various prediction tasks.
  arXiv  Detail & Related papers  (2020-09-24T06:31:14Z)

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.