Ant Colony
Watch AI ants learn to find food using Q-learning. See pheromone trails emerge as the colony optimizes its foraging strategy.
About Ant Colony
Hundreds of ants wander a grid at random — then, slowly, they stop wandering. Watch as Q-learning shapes each ant's decisions in real time, and pheromone trails bloom across the screen like ink spreading through water. What starts as chaos organizes itself into efficient foraging routes, entirely without a script or a programmer telling each ant what to do.
This is interactive simulation as a teaching tool. The colony demonstrates reinforcement learning the way a textbook can't: you see the reward signals accumulate, watch bad paths get abandoned, and notice the moment the colony "figures it out." If you enjoy watching AI steer itself toward a goal, Self-Driving Car Sim offers a satisfying companion experiment with a very different algorithm.
No controls to master — just observe, or refresh to watch the learning process restart from scratch. It runs best on a desktop browser where you can keep it open in a corner and glance over as the trails evolve.
Look inside
How to use
Watch ants learn to find food using artificial intelligence. The simulation runs automatically - ants start with no knowledge and gradually become better at finding efficient paths to food sources. • **Objective**: Observe how ants learn to collect food and return it to their nest (center of grid) using reinforcement learning • **Controls**: No controls needed - the simulation runs automatically. Just watch the ants explore and learn. • **What you'll see**: - Black dots: Ants without food (exploring) - Red dots: Ants carrying food (returning to nest) - Colored squares: Food sources that deplete over time - Purple trails: Pheromone paths left by successful ants • **Key mechanics**: - Ants start at the center nest and explore randomly - When they find food, they carry it back and leave pheromone trails - Other ants can follow these chemical trails to find food faster - The AI learns which actions lead to rewards (finding food, returning home) - After 42 episodes, the exploration rate stabilizes and ants become very efficient • **Side panel shows**: Current episode, steps taken, total food collected, rewards earned, and a graph tracking food levels at each source over time The simulation demonstrates swarm intelligence - how simple individual behaviors create complex group problem-solving.
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