VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
This tool addresses a domain-specific problem for neuroevolution researchers by providing incremental improvements in visualization to aid analysis and inspire innovations.
The paper tackles the challenge of analyzing and interpreting high-dimensional neuroevolution processes by presenting VINE, an interactive data visualization tool that helps researchers and end-users understand and explore algorithms like evolution strategies and genetic algorithms through visualizations of evolving agent behavior over generations.
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.