Ângelo Miguel Rodrigues Morgado , Nuno Gonçalo Coelho Costa Pombo
{"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}
引用次数: 0
Abstract
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.
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
