An AI-driven experiment based on reinforcement learning can be visualized as a decision tree. Due to the complexity of these graphs, specialized display techniques may be necessary. Strategies such as reducing labels, color-coding links based on outcomes, consolidating branch results, and providing detailed information on request enhance the clarity of the visualization. Below is an example of a decision tree with random data. This interactive demo has been developed using our powerful JavaScript/TypeScript diagramming library. Seamlessly integratable with popular frameworks such as React, Angular, Vue, and Svelte, our library empowers developers to create rich and interactive diagramming applications with ease.