CARLA-GymDrive在健身房环境中为 Carla 模拟器生成自动驾驶情节

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2024-11-15 DOI:10.1016/j.softx.2024.101955

Ângelo Miguel Rodrigues Morgado , Nuno Gonçalo Coelho Costa Pombo

{"title":"CARLA-GymDrive在健身房环境中为 Carla 模拟器生成自动驾驶情节","authors":"Ângelo Miguel Rodrigues Morgado , Nuno Gonçalo Coelho Costa Pombo","doi":"10.1016/j.softx.2024.101955","DOIUrl":null,"url":null,"abstract":"<div><div>CARLA-GymDrive is a powerful framework designed to facilitate reinforcement learning experiments in autonomous driving using the Carla simulator. By providing a gymnasium-like environment, it offers an intuitive and efficient platform for training driving agents using reinforcement learning techniques. It includes features such as scenario configuration to ensure that the training/test suite is adequate without requiring any code. Additionally, it boasts other features such as custom sensor configuration and compatibility with training libraries like Stable-Baselines3. This tool aims to increase researchers’ productivity by abstracting them from the complex code of the simulator, allowing them to focus on their research.</div></div>","PeriodicalId":21905,"journal":{"name":"SoftwareX","volume":"28 ","pages":"Article 101955"},"PeriodicalIF":2.4000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CARLA-GymDrive: Autonomous driving episode generation for the Carla simulator in a gym environment\",\"authors\":\"Ângelo Miguel Rodrigues Morgado , Nuno Gonçalo Coelho Costa Pombo\",\"doi\":\"10.1016/j.softx.2024.101955\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>CARLA-GymDrive is a powerful framework designed to facilitate reinforcement learning experiments in autonomous driving using the Carla simulator. By providing a gymnasium-like environment, it offers an intuitive and efficient platform for training driving agents using reinforcement learning techniques. It includes features such as scenario configuration to ensure that the training/test suite is adequate without requiring any code. Additionally, it boasts other features such as custom sensor configuration and compatibility with training libraries like Stable-Baselines3. This tool aims to increase researchers’ productivity by abstracting them from the complex code of the simulator, allowing them to focus on their research.</div></div>\",\"PeriodicalId\":21905,\"journal\":{\"name\":\"SoftwareX\",\"volume\":\"28 \",\"pages\":\"Article 101955\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SoftwareX\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S235271102400325X\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SoftwareX","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S235271102400325X","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}

引用次数: 0

摘要

CARLA-GymDrive 是一个功能强大的框架，旨在利用 Carla 模拟器促进自动驾驶中的强化学习实验。通过提供类似体育馆的环境，它为使用强化学习技术训练驾驶代理提供了一个直观、高效的平台。它包括场景配置等功能，可确保训练/测试套件的充分性，而无需编写任何代码。此外，它还具有其他功能，如自定义传感器配置和与 Stable-Baselines3 等训练库兼容。该工具旨在将研究人员从模拟器的复杂代码中抽象出来，从而提高他们的工作效率，让他们能够专注于研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

CARLA-GymDrive: Autonomous driving episode generation for the Carla simulator in a gym environment

CARLA-GymDrive is a powerful framework designed to facilitate reinforcement learning experiments in autonomous driving using the Carla simulator. By providing a gymnasium-like environment, it offers an intuitive and efficient platform for training driving agents using reinforcement learning techniques. It includes features such as scenario configuration to ensure that the training/test suite is adequate without requiring any code. Additionally, it boasts other features such as custom sensor configuration and compatibility with training libraries like Stable-Baselines3. This tool aims to increase researchers’ productivity by abstracting them from the complex code of the simulator, allowing them to focus on their research.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

期刊介绍： SoftwareX aims to acknowledge the impact of software on today''s research practice, and on new scientific discoveries in almost all research domains. SoftwareX also aims to stress the importance of the software developers who are, in part, responsible for this impact. To this end, SoftwareX aims to support publication of research software in such a way that: The software is given a stamp of scientific relevance, and provided with a peer-reviewed recognition of scientific impact; The software developers are given the credits they deserve; The software is citable, allowing traditional metrics of scientific excellence to apply; The academic career paths of software developers are supported rather than hindered; The software is publicly available for inspection, validation, and re-use. Above all, SoftwareX aims to inform researchers about software applications, tools and libraries with a (proven) potential to impact the process of scientific discovery in various domains. The journal is multidisciplinary and accepts submissions from within and across subject domains such as those represented within the broad thematic areas below: Mathematical and Physical Sciences; Environmental Sciences; Medical and Biological Sciences; Humanities, Arts and Social Sciences. Originating from these broad thematic areas, the journal also welcomes submissions of software that works in cross cutting thematic areas, such as citizen science, cybersecurity, digital economy, energy, global resource stewardship, health and wellbeing, etcetera. SoftwareX specifically aims to accept submissions representing domain-independent software that may impact more than one research domain.