Victor Le
About Victor Le
Victor Le is an AI R&D Engineer with extensive experience in reinforcement learning and software development. He has worked at various companies, including InstaDeep, EDF, and Bull, and has co-founded 11th District Games.
Work at InstaDeep
Victor Le has been employed at InstaDeep as an AI R&D Engineer since 2018. In this role, he focuses on developing advanced artificial intelligence solutions, particularly in the realm of reinforcement learning. He has successfully developed a Deep Reinforcement Learning system that demonstrates superhuman performance in solving NP-hard decision-making problems. His contributions at InstaDeep emphasize the application of state-of-the-art techniques to real-world challenges.
Previous Employment Experience
Prior to his current position, Victor Le held several roles in the technology sector. He worked as a C# VBA Developer at EDF for one year in La Défense from 2014 to 2015. Following this, he served as a Fullstack Developer at Steery for ten months in the Paris Area from 2015 to 2016. He also worked as a Software Engineer at Bull from 2016 to 2018 in the Région de Grenoble. Additionally, he co-founded 11th District Games, where he contributed to the development of gaming applications from 2015 to 2019.
Education and Expertise
Victor Le holds a Diplôme d'ingénieur in Mathematics and Computer Science from Ecole Nationale Supérieure d'Informatique et de Mathématiques Appliquées de Grenoble, which he completed from 2015 to 2018. He also obtained a DUT in Computer Science from Université Paris Descartes between 2013 and 2015. Furthering his education, he studied Computer Science at Ecole polytechnique fédérale de Lausanne for one year in 2017 to 2018. His educational background provides a strong foundation for his expertise in reinforcement learning and planning techniques.
Research Contributions
Victor Le contributed to the research paper titled 'Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX,' which was presented at ICLR 2024. This work showcases his involvement in advancing the field of reinforcement learning and highlights his commitment to developing scalable solutions for complex problems.