The rules of tag are simple: catch your opponent before they reach safety. But when the players are autonomous drones, the familiar playground game becomes a serious test of artificial intelligence, machine learning and split-second decision-making.
Researchers at Sandia National Laboratories have used reinforcement learning, a type of machine learning, to teach drones to play a strategic version of tag. The experiment is more than a technological game. It could help researchers develop better autonomous systems that can pursue targets, avoid threats and cooperate with other machines in rapidly changing environments.
The research is part of AutonomyNM, a larger Sandia effort focused on exploring how artificial intelligence and machine learning can improve autonomous systems. The team's work was recently presented as a conference paper at the 2026 IEEE International Conference on Robotics and Automation.
Turning a Simple Game Into an AI Challenge
In Sandia's version of tag, there were four drones. Two drones acted as pursuers, while two others were evaders.
The pursuers were responsible for protecting a safe area, or "base." The evading drones had to work together to reach that base without being caught.
But there was an interesting twist.
Instead of simply trying to outrun the pursuers, the evading drones learned to cooperate. Their goal was to maneuver in ways that could cause the pursuing drones to collide with each other. If successful, the evaders could reach the base without being tagged.
This created a complex problem involving movement, prediction, timing and cooperation.
For humans, such decisions can be made intuitively. For a machine, however, every movement must be calculated based on available information.
That is where reinforcement learning becomes useful.
Reinforcement Learning: Teaching Machines Through Rewards
Reinforcement learning works somewhat like training a puppy.
When a puppy performs the desired behavior, it receives a reward. When it does something undesirable, it receives a negative response. Over time, the puppy learns which actions produce better results.
A reinforcement-learning algorithm works in a similar way.
The researchers created a virtual environment where the drones could repeatedly practice the game. Successful actions received rewards, while undesirable behaviors were penalized.
Christian Llanes, a Sandia intern and robotics doctoral student at the Georgia Institute of Technology, explained that one of the biggest challenges is reward shaping.
Reward shaping means deciding what behaviors the machine should be rewarded or penalized for—and determining how strongly each behavior should affect the learning process.
If the reward system is poorly designed, an AI system may learn an unexpected strategy that technically achieves its goal but does not behave as researchers intended.
Letting AI Discover the Strategy
One major advantage of reinforcement learning is that researchers do not need to manually program every possible situation.
The real world is unpredictable. A drone may encounter another aircraft, an unexpected movement, changing wind conditions or communication delays. Programming a specific response for every possible situation would be extremely difficult.
Instead, reinforcement learning allows the system to discover useful strategies through repeated simulation.
Spencer Jensen, a Sandia computer scientist and lead researcher, said reinforcement learning can make complicated problems easier by allowing an algorithm to learn behavior that would be difficult to program directly.
In this experiment, the reinforcement-learning system controlled the guidance module of the evading drones. The guidance system determines the path the drone should follow, while the control system makes the drone physically follow that path.
Interestingly, the pursuers were not trained using reinforcement learning. They used a more traditional technique called proportional navigation, which attempts to intercept a moving target.
This allowed researchers to compare the flexible behavior of the learning-based evaders against a more straightforward pursuit strategy.
From Computer Simulation to Real Drones
Training the drones in a computer was only the first step.
The researchers then moved the learned algorithm from simulation onto physical drones.
This transition created a major challenge known as the simulation-to-reality gap.
A computer simulation can model many aspects of a drone, but it cannot perfectly reproduce every detail of the physical world. Real drones experience factors that can be difficult to represent accurately in software.
For example, battery power changes over time and can affect motor and rotor performance. One drone can also produce air movement that affects another nearby drone.
There can also be small communication and processing delays that are difficult to reproduce perfectly in a simulation.
These seemingly minor differences can significantly change how an autonomous system behaves.
Why Small Drones Were the Perfect Test Platform
To deal with these challenges, the researchers used small and inexpensive commercial quadrotor drones.
The drones were approximately 3.5 inches (8.9 centimeters) wide and weighed less than a chocolate bar.
Their small size offered an important advantage: crashes were relatively inexpensive.
Instead of risking large and costly aircraft, researchers could repeatedly test their algorithms on small drones. If a drone crashed, it might only break an inexpensive plastic component.
That made experimentation faster and safer.
Researchers could modify the algorithm, test it, identify problems and make improvements without the high costs associated with larger drones.
The team also studied the physical drones carefully before building their simulation models. By creating more accurate models, they were able to reduce some of the differences between the virtual environment and the real world.
A High-Tech Laboratory for Autonomous Drones
The physical experiments were conducted in Sandia's drone laboratory at the Center for Advanced Manufacturing and Innovation (CAMINO).
The facility uses an advanced infrared motion-capture system to track the drones' positions with high precision. Similar motion-capture technology is commonly used in film production.
This allows researchers to understand exactly how the drones move during experiments.
The facility also provides an environment where researchers can test autonomous systems on inexpensive hardware before moving to more expensive and higher-consequence platforms.
That approach is important because autonomous systems can behave differently in the real world than they do in simulations.
Finding those problems early can save significant time and resources.
Why This Research Matters
Although a game of drone tag may sound like entertainment, the underlying technology has serious potential.
Autonomous drones could eventually be used in situations where machines need to react faster than humans can manually control them. They may need to pursue moving objects, avoid threats, coordinate with other machines or protect important locations.
The research could therefore contribute to future autonomous systems designed for applications such as protecting critical infrastructure from hostile drones and other national-security scenarios.
However, the broader importance extends beyond security.
The same concepts could be useful in robotics, search-and-rescue operations, industrial automation and coordinated drone systems. Whenever multiple autonomous machines must make decisions in uncertain and rapidly changing environments, reinforcement learning could provide a flexible approach.
The Future of Autonomous Machines
Sandia's drone-tag experiment demonstrates how artificial intelligence can transform a simple idea into a sophisticated robotics challenge.
The drones were not simply following a list of programmed instructions. Through reinforcement learning, they learned strategies that allowed them to cooperate and respond to their opponents.
At the same time, the experiment highlights an important reality of modern AI: teaching a machine in simulation is only part of the challenge. Making that intelligence work reliably on real hardware can be much harder.
By combining machine learning, accurate simulation and inexpensive physical testing, researchers are working to close that gap.
The ultimate goal is not to create drones that are simply good at playing tag. It is to develop autonomous machines that can understand changing situations, make decisions quickly and work together effectively.
What began as a playground game could therefore become a valuable step toward the next generation of intelligent robots.
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