Multi-Agent Simulator
Experiment with agent systems and visualize emergent behaviors
Your Progress
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Theory becomes practice when you test agent systems in realistic scenarios. Disaster rescue, warehouse logistics, and traffic management each present unique coordination challenges. Run simulations to see how your agent designs perform under real-world constraints.
Available Test Scenarios
Disaster Rescue
Coordinate rescue drones to locate and assist survivors
Warehouse Logistics
Optimize robot fleet for order fulfillment
Traffic Management
Self-driving cars coordinate at intersections
Interactive: Scenario Simulator
Choose a scenario and run the simulation. Observe performance metrics and identify coordination challenges.
SELECT SCENARIO
OBJECTIVES
- ✓ Locate 12 survivors
- ✓ Coordinate coverage
- ✓ Avoid collisions
CHALLENGES
- ⚠ Limited battery
- ⚠ Communication range
- ⚠ Dynamic obstacles
Interpreting Results
Percentage of scenario objectives achieved. High scores indicate effective goal pursuit.
Resource utilization and speed. Measures how optimally agents perform tasks.
Quality of agent collaboration. High scores mean smooth teamwork and minimal conflicts.
Number of agents that failed during execution. Lower is better for robustness.
💡 Key Insight
Simulations reveal emergent weaknesses. A rescue team might complete 95% of tasks but have terrible efficiency due to poor path planning. Warehouse robots might be efficient but fail coordination, causing collisions. Traffic systems might coordinate well but achieve low throughput. Testing exposes gaps between theoretical design and practical performance—iterate based on what you observe.