AI Visual Simulations

An interactive collection of browser-based visualizations that transform complex AI learning processes into engaging, observable experiences. This project makes artificial intelligence algorithms tangible by displaying their decision-making and evolution in real-time.

Featured Simulations

• Snake: Watch as an AI learns optimal pathfinding strategies in the classic Snake game, adapting its behavior to maximize score while avoiding collisions.
• Self-driving Cars: Observe neural networks evolve to navigate increasingly complex road systems, learning to interpret sensor data and make driving decisions without human intervention.
• XOR: Visualize how neural networks solve the classic XOR problem, providing an intuitive entry point to understand how AI learns non-linear patterns.
• Flappy Bird: See reinforcement learning in action as AI agents master the timing and precision needed to navigate through obstacles.
• Pole Balancing: Experience the classic control theory problem of inverted pendulum balancing, where algorithms learn to maintain equilibrium through continuous adjustment.
• Prey & Predator Simulation: Witness evolutionary algorithms at work in a simulated ecosystem, where competing species develop survival strategies through natural selection principles.

Each simulation provides transparency into the "black box" of AI learning, allowing users to witness the progression from random actions to sophisticated decision-making, all rendered through accessible browser-based interfaces.