Automatic Design Method of Building Pipeline Layout Based on Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2305.10760v1
• Date: Thu, 18 May 2023 07:01:58 GMT
• Title: Automatic Design Method of Building Pipeline Layout Based on Deep Reinforcement Learning
• Abstract summary: Currently, pipeline layout is designed manually by engineers. We propose a method for generating three-dimensional layout of pipelines based on deep reinforcement learning (DRL) Our results demonstrate that DRL models can complete the pipeline layout task in space in a much shorter time than traditional algorithms.
• Score: 2.2284196209013345
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The layout design of pipelines is a critical task in the construction industry. Currently, pipeline layout is designed manually by engineers, which is time-consuming and laborious. Automating and streamlining this process can reduce the burden on engineers and save time. In this paper, we propose a method for generating three-dimensional layout of pipelines based on deep reinforcement learning (DRL). Firstly, we abstract the geometric features of space to establish a training environment and define reward functions based on three constraints: pipeline length, elbow, and installation distance. Next, we collect data through interactions between the agent and the environment and train the DRL model. Finally, we use the well-trained DRL model to automatically design a single pipeline. Our results demonstrate that DRL models can complete the pipeline layout task in space in a much shorter time than traditional algorithms while ensuring high-quality layout outcomes.

Related papers

• JigsawRL: Assembling RL Pipelines for Efficient LLM Post-Training [18.390343908577318]
  JigsawRL decomposes each pipeline into a Sub-Stage Graph that exposes the intra-stage and inter-worker imbalance hidden by stage-level systems.<n>J JigsawRL achieves up to 1.85x throughput over Verl on synchronous RL, 1.54x over StreamRL and AReaL on asynchronous RL, and supports heterogeneous pipelines with moderate latency trade-off.
  arXiv  Detail & Related papers  (2026-04-26T18:45:31Z)
• PARM: Pipeline-Adapted Reward Model [60.769414637325326]
  Reward models (RMs) are central to aligning large language models (LLMs) with human preferences, powering advanced decoding strategies.<n>While most prior work focuses on single-step generation, real-world applications increasingly adopt multi-stage pipelines.<n>We investigate this through code generation for optimization, constructing a pipeline that integrates reward models into both formulation and solution stages.
  arXiv  Detail & Related papers  (2026-04-20T14:29:08Z)
• CIPHER: Culvert Inspection through Pairwise Frame Selection and High-Efficiency Reconstruction [53.9319127526742]
  We present an efficient RGB-based 3D reconstruction pipeline for culvert-like structures in visually repetitive environments.<n>Our approach first selects informative frame pairs to maximize viewpoint diversity while ensuring valid correspondence matching.<n> Experiments demonstrate that our method effectively generates accurate 3D reconstructions and depth maps, enhancing culvert inspection efficiency with minimal human intervention.
  arXiv  Detail & Related papers  (2026-03-14T23:00:19Z)
• A Unified Experimental Architecture for Informative Path Planning: from Simulation to Deployment with GuadalPlanner [69.43049144653882]
  This paper introduces a unified architecture that decouples high-level decision-making from vehicle-specific control.<n>The proposed architecture is realized through GuadalPlanner, which defines standardized interfaces between planning, sensing, and vehicle execution.
  arXiv  Detail & Related papers  (2026-02-11T10:02:31Z)
• AdaPtis: Reducing Pipeline Bubbles with Adaptive Pipeline Parallelism on Heterogeneous Models [59.7059443712562]
  AdaPtis is a training system for large language models (LLMs) that supports adaptive pipeline parallelism.<n>Extensive experiments show that AdaPtis achieves an average speedup of 1.42x (up to 2.14x) over Megatron-LM I-1F1B.
  arXiv  Detail & Related papers  (2025-09-28T08:05:13Z)
• cadrille: Multi-modal CAD Reconstruction with Online Reinforcement Learning [55.16668009268005]
  We propose a multi-modal CAD reconstruction model that simultaneously processes all three input modalities.<n>Inspired by large language model (LLM) training paradigms, we adopt a two-stage pipeline: supervised fine-tuning (SFT) on large-scale procedurally generated data, followed by reinforcement learning (RL) fine-tuning using online feedback, obtained programatically.<n>In the DeepCAD benchmark, our SFT model outperforms existing single-modal approaches in all three input modalities simultaneously.
  arXiv  Detail & Related papers  (2025-05-28T22:32:31Z)
• StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation [55.75008325187133]
  Reinforcement learning (RL) has become the core post-training technique for large language models (LLMs) StreamRL is designed with disaggregation from first principles to address two types of performance bottlenecks. Experiments show that StreamRL improves throughput by up to 2.66x compared to existing state-of-the-art systems.
  arXiv  Detail & Related papers  (2025-04-22T14:19:06Z)
• Self-Learning-Based Optimization for Free-form Pipe Routing in Aeroengine with Dynamic Design Environment [5.128220263390701]
  This study proposes a self-learning-based method (SLPR) for optimizing free-form pipe routing in aeroengines. The agent within SLPR iteratively refines pipe routing and accumulates the design knowledge through interaction with the environment. Results in both static and dynamic design environments demonstrate that SLPR outperforms three representative baselines in terms of the pipe length reduction, the adherence to layout rules, the path complexity, and the computational efficiency.
  arXiv  Detail & Related papers  (2025-03-20T13:45:13Z)
• SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins [78.53885607559958]
  We propose SCoTT, a wireless-aware path planning framework.<n>We show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories.<n>We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations.
  arXiv  Detail & Related papers  (2024-11-27T10:45:49Z)
• FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
  Decentralized training faces significant challenges regarding system design and efficiency. We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs) We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
  arXiv  Detail & Related papers  (2024-10-16T16:13:19Z)
• OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing Operators [57.145175475579315]
  This topic spans various techniques, from structured pruning to neural architecture search, encompassing both pruning and erasing operators perspectives. We introduce the third-generation Only-Train-Once (OTOv3), which first automatically trains and compresses a general DNN through pruning and erasing operations. Our empirical results demonstrate the efficacy of OTOv3 across various benchmarks in structured pruning and neural architecture search.
  arXiv  Detail & Related papers  (2023-12-15T00:22:55Z)
• Deep Pipeline Embeddings for AutoML [11.168121941015015]
  AutoML is a promising direction for democratizing AI by automatically deploying Machine Learning systems with minimal human expertise. Existing Pipeline Optimization techniques fail to explore deep interactions between pipeline stages/components. This paper proposes a novel neural architecture that captures the deep interaction between the components of a Machine Learning pipeline.
  arXiv  Detail & Related papers  (2023-05-23T12:40:38Z)
• Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications [2.496282558123411]
  This paper explores continuous-time control for target-driven navigation to satisfy complex high-level tasks expressed as linear temporal logic (LTL) We propose a model-free synthesis framework using deep reinforcement learning (DRL) where the underlying dynamic system is unknown (an opaque box)
  arXiv  Detail & Related papers  (2022-10-03T18:32:20Z)
• ARLO: A Framework for Automated Reinforcement Learning [36.91138310444942]
  We propose a framework, namely ARLO: Automated Reinforcement Learning, to construct automated pipelines for AutoRL. We provide a Python implementation of such pipelines, released as an open-source library. Our implementation has been tested on an illustrative LQG domain and on classic MuJoCo environments.
  arXiv  Detail & Related papers  (2022-05-20T19:16:04Z)
• Where Is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines [77.45213180689952]
  Preprocessing pipelines in deep learning aim to provide sufficient data throughput to keep the training processes busy. We introduce a new perspective on efficiently preparing datasets for end-to-end deep learning pipelines. We obtain an increased throughput of 3x to 13x compared to an untuned system.
  arXiv  Detail & Related papers  (2022-02-17T14:31:58Z)
• Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach [93.67588414950656]
  In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a 3D environment. We present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.
  arXiv  Detail & Related papers  (2021-07-23T03:33:29Z)
• Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines [48.7576911714538]
  The proposed approach is aimed to automate the design of composite machine learning pipelines. It designs the pipelines with a customizable graph-based structure, analyzes the obtained results, and reproduces them. The software implementation on this approach is presented as an open-source framework.
  arXiv  Detail & Related papers  (2021-06-26T23:19:06Z)
• PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers [47.194426122333205]
  PipeTransformer is a distributed training algorithm for Transformer models. It automatically adjusts the pipelining and data parallelism by identifying and freezing some layers during the training. We evaluate PipeTransformer using Vision Transformer (ViT) on ImageNet and BERT on GLUE and SQuAD datasets.
  arXiv  Detail & Related papers  (2021-02-05T13:39:31Z)
• AutoWeka4MCPS-AVATAR: Accelerating Automated Machine Learning Pipeline Composition and Optimisation [13.116806430326513]
  We propose a novel method to evaluate the validity of ML pipelines, without their execution, using a surrogate model (AVATAR) The AVATAR generates a knowledge base by automatically learning the capabilities and effects of ML algorithms on datasets' characteristics. Instead of executing the original ML pipeline to evaluate its validity, the AVATAR evaluates its surrogate model constructed by capabilities and effects of the ML pipeline components.
  arXiv  Detail & Related papers  (2020-11-21T14:05:49Z)

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.