{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/vine-an-open-source-interactive-data","title":"VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution","arxiv_id":"1805.01141","date":"2018-05-03","proceeding":null,"authors":["Rui Wang","Jeff Clune","Kenneth O. Stanley"],"abstract":"Recent advances in deep neuroevolution have demonstrated that evolutionary\nalgorithms, such as evolution strategies (ES) and genetic algorithms (GA), can\nscale to train deep neural networks to solve difficult reinforcement learning\n(RL) problems. However, it remains a challenge to analyze and interpret the\nunderlying process of neuroevolution in such high dimensions. To begin to\naddress this challenge, this paper presents an interactive data visualization\ntool called VINE (Visual Inspector for NeuroEvolution) aimed at helping\nneuroevolution researchers and end-users better understand and explore this\nfamily of algorithms. VINE works seamlessly with a breadth of neuroevolution\nalgorithms, including ES and GA, and addresses the difficulty of observing the\nunderlying dynamics of the learning process through an interactive\nvisualization of the evolving agent's behavior characterizations over\ngenerations. As neuroevolution scales to neural networks with millions or more\nconnections, visualization tools like VINE that offer fresh insight into the\nunderlying dynamics of evolution become increasingly valuable and important for\ninspiring new innovations and applications.","url_abs":"http://arxiv.org/abs/1805.01141v1","url_pdf":"http://arxiv.org/pdf/1805.01141v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"vine-an-open-source-interactive-data","repo_url":"https://github.com/uber-common/deep-neuroevolution","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"data-visualization","task_name":"Data Visualization"},{"task_slug":"evolutionary-algorithms","task_name":"Evolutionary Algorithms"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}