The developer transitioned an evolutionary simulation project from a React and Python stack to Unity and C#. The original architecture relied on a client-server model using WebSockets, which caused performance issues and synchronization errors during real-time simulation.
Unity provides built-in tools for physics, collisions, and raycasting, which replace the manual trigonometric calculations previously required for agent vision. The developer also utilizes the UI Toolkit for rendering statistics and debugging information.
The simulation features agents controlled by neural networks. The current architecture uses a 7-6-3 structure, processing inputs such as vision sectors, hunger, fatigue, time of day, and environmental danger. A third output was added to the neural network to manage social interactions, categorized into aggressive, peaceful, or neutral actions.
During development, the author encountered a logic error in the epoch timer that caused rapid spawning of thousands of agents. Unlike the previous web-based prototype, the Unity engine maintained stability under the increased load.
