VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution


    Recent advances in deep neuroevolution have demonstrated that evolutionary algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can scale to train deep neural networks to solve difficult reinforcement learning (RL) problems. However, it remains a challenge to analyze and interpret the underlying process of neuroevolution in such high dimensions. To begin to address this challenge, this paper presents an interactive data visualization tool called VINE (Visual Inspector for NeuroEvolution) aimed at helping neuroevolution researchers and end-users better understand and explore this family of algorithms. VINE works seamlessly with a breadth of neuroevolution algorithms, including ES and GA, and addresses the difficulty of observing the underlying dynamics of the learning process through an interactive visualization of the evolving agent’s behavior characterizations over generations. As neuroevolution scales to neural networks with millions or more connections, visualization tools like VINE that offer fresh insight into the underlying dynamics of evolution become increasingly valuable and important for inspiring new innovations and applications.


    Rui Wang, Jeff Clune, Kenneth O. Stanley


    Visualization Workshop at GECCO 2018

    Full Paper

    ‘VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution’ (PDF)

    Uber AI

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    Rui Wang
    Rui Wang is a senior research scientist with Uber AI. He is passionate about advancing the state of the art of machine learning and AI, and connecting cutting-edge advances to the broader business and products at Uber. His recent work at Uber was published on leading international conferences in machine learning and AI (ICML, IJCAI, GECCO, etc.), won a Best Paper Award at GECCO 2019, and was covered by technology media such as Science, Wired, VentureBeat, and Quanta Magazine.
    Jeff Clune
    Jeff Clune is the former Loy and Edith Harris Associate Professor in Computer Science at the University of Wyoming, a Senior Research Manager and founding member of Uber AI Labs, and currently a Research Team Leader at OpenAI. Jeff focuses on robotics and training neural networks via deep learning and deep reinforcement learning. He has also researched open questions in evolutionary biology using computational models of evolution, including studying the evolutionary origins of modularity, hierarchy, and evolvability. Prior to becoming a professor, he was a Research Scientist at Cornell University, received a PhD in computer science and an MA in philosophy from Michigan State University, and received a BA in philosophy from the University of Michigan. More about Jeff’s research can be found at
    Kenneth O. Stanley
    Before joining Uber AI Labs full time, Ken was an associate professor of computer science at the University of Central Florida (he is currently on leave). He is a leader in neuroevolution (combining neural networks with evolutionary techniques), where he helped invent prominent algorithms such as NEAT, CPPNs, HyperNEAT, and novelty search. His ideas have also reached a broader audience through the recent popular science book, Why Greatness Cannot Be Planned: The Myth of the Objective.