XRoute Environment: A Novel Reinforcement Learning Environment for Routing

• URL: http://arxiv.org/abs/2305.13823v2
• Date: Mon, 5 Jun 2023 07:53:23 GMT
• Title: XRoute Environment: A Novel Reinforcement Learning Environment for Routing
• Authors: Zhanwen Zhou, Hankz Hankui Zhuo, Xiaowu Zhang, Qiyuan Deng
• Abstract summary: We introduce the XRoute Environment, a new reinforcement learning environment. Agents are trained to select and route nets in an advanced, end-to-end routing framework. The resulting environment is challenging, easy to use, customize and add additional scenarios.
• Score: 8.797544401458476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Routing is a crucial and time-consuming stage in modern design automation flow for advanced technology nodes. Great progress in the field of reinforcement learning makes it possible to use those approaches to improve the routing quality and efficiency. However, the scale of the routing problems solved by reinforcement learning-based methods in recent studies is too small for these methods to be used in commercial EDA tools. We introduce the XRoute Environment, a new reinforcement learning environment where agents are trained to select and route nets in an advanced, end-to-end routing framework. Novel algorithms and ideas can be quickly tested in a safe and reproducible manner in it. The resulting environment is challenging, easy to use, customize and add additional scenarios, and it is available under a permissive open-source license. In addition, it provides support for distributed deployment and multi-instance experiments. We propose two tasks for learning and build a full-chip test bed with routing benchmarks of various region sizes. We also pre-define several static routing regions with different pin density and number of nets for easier learning and testing. For net ordering task, we report baseline results for two widely used reinforcement learning algorithms (PPO and DQN) and one searching-based algorithm (TritonRoute). The XRoute Environment will be available at https://github.com/xplanlab/xroute_env.

Related papers

• Coarse-to-Fine Q-attention with Learned Path Ranking [95.00518278458908]
  We propose Learned Path Ranking (LPR), a method that accepts an end-effector goal pose, and learns to rank a set of goal-reaching paths. In addition to benchmarking our approach across 16 RLBench tasks, we also learn real-world tasks, tabula rasa, in 10-15 minutes, with only 3 demonstrations.
  arXiv  Detail & Related papers  (2022-04-04T15:23:14Z)
• Systematic Comparison of Path Planning Algorithms using PathBench [55.335463666037086]
  Path planning is an essential component of mobile robotics. Development of learning-based path planning algorithms has been experiencing rapid growth. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future path planning algorithms.
  arXiv  Detail & Related papers  (2022-03-07T01:52:57Z)
• Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization [49.207538634692916]
  We propose a new algorithm for circuit routing, named Ranking Cost, to form an efficient and trainable router. In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths. Our algorithm is trained in an end-to-end manner and does not use any artificial data or human demonstration.
  arXiv  Detail & Related papers  (2021-10-08T07:22:45Z)
• Learning Enhanced Optimisation for Routing Problems [3.747361228408185]
  "Learning to Guide Local Search" (L2GLS) is a learning-based approach for routing problems. L2GLS combines local search (LS) operators' strengths with penalty terms to escape local optimals. We show that L2GLS achieves the new state-of-the-art results on larger TSP and CVRP over other machine learning methods.
  arXiv  Detail & Related papers  (2021-09-17T04:47:26Z)
• PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms [59.3879573040863]
  Path planning is a key component in mobile robotics. Few attempts have been made to benchmark the algorithms holistically or unify their interface. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future path planning algorithms.
  arXiv  Detail & Related papers  (2021-05-04T21:48:18Z)
• Machine Learning Applications in the Routing in Computer Networks [0.9020406183127511]
  Development of routing algorithms is of clear importance as the volume of Internet traffic continues to increase. We surveyed both centralized and decentralized ML routing architectures. We implemented two routing protocols within 14 surveyed routing algorithms and verified the efficacy of their results.
  arXiv  Detail & Related papers  (2021-04-05T15:08:35Z)
• Learn-n-Route: Learning implicit preferences for vehicle routing [9.434400627011108]
  We investigate a learning decision support system for vehicle routing, where the routing engine learns implicit preferences that human planners have when manually creating route plans (or routings) The goal is to use these learned subjective preferences on top of the distance-based objective criterion in vehicle routing systems.
  arXiv  Detail & Related papers  (2021-01-11T14:57:46Z)
• CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search [102.67142711824748]
  CATCH is a novel Context-bAsed meTa reinforcement learning algorithm for transferrable arChitecture searcH. The combination of meta-learning and RL allows CATCH to efficiently adapt to new tasks while being agnostic to search spaces. It is also capable of handling cross-domain architecture search as competitive networks on ImageNet, COCO, and Cityscapes are identified.
  arXiv  Detail & Related papers  (2020-07-18T09:35:53Z)
• Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads [11.646744408920764]
  Auto-MAP is a framework for exploring distributed execution plans for workloads. It can automatically discovering fast parallelization strategies through reinforcement learning on IR level of deep learning models. Our evaluation shows that Auto-MAP can find the optimal solution in two hours, while achieving better throughput on several NLP and convolution models.
  arXiv  Detail & Related papers  (2020-07-08T12:38:03Z)
• Learning Dynamic Routing for Semantic Segmentation [86.56049245100084]
  This paper studies a conceptually new method to alleviate the scale variance in semantic representation, named dynamic routing. The proposed framework generates data-dependent routes, adapting to the scale distribution of each image. To this end, a differentiable gating function, called soft conditional gate, is proposed to select scale transform paths on the fly.
  arXiv  Detail & Related papers  (2020-03-23T17:22:14Z)

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.