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.
Demo instructions
Hover over the nodes to get a detailed view of the parts of the decision tree.
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