Using AI to train teams of robots to work together



Individual agents, such as robots or drones, can cooperate and finish a task when communication channels are open. What happens, though, if their technology is insufficient or the signals are jammed, making communication impossible? Researchers from the University of Illinois at Urbana-Champaign began with this more challenging task. They created a technique using multi-agent reinforcement learning, a form of artificial intelligence, to teach many agents to cooperate.

Huy Tran, an aeronautical engineer at Illinois, noted that it is simpler when agents can communicate with one another. "But we wanted to achieve this in a decentralized manner, so that they don't communicate with one another. We also concentrated on circumstances in which it is unclear what the various duties or responsibilities of the agents should be."

Because it's unclear what one agent should do in contrast to another agent, Tran claimed that this scenario is far more complicated and a harder difficulty.

The intriguing topic, according to Tran, is how humans can gradually learn to work together to complete a goal.

By developing a utility function that alerts the agent when it is acting in a way that is beneficial to the team or useful, Tran and his colleagues employed machine learning to find a solution to this issue.

With team goals, "it's difficult to determine who helped us win," he remarked. "We created a machine learning method that enables us to recognize when a single agent contributes to the overall team goal. If you compare it to sports, one soccer player may score, but we also want to know about the teammate's contributions, such as assists. Understanding these delayed impacts is challenging."

The algorithms that the researchers used can also spot when an agent or robot is acting in a way that isn't helpful to the end result. The robot simply chose to do something that isn't helpful to the end result, not necessarily anything that was bad.

They used simulated games like Capture the Flag and StarCraft, a well-known computer game, to evaluate their algorithms.

Watch Huy Tran demonstrate related research utilizing deep reinforcement learning to assist robots in determining their next move in the game of Capture the Flag.

StarCraft can be a little more unexpected, so we were happy to learn that our strategy also worked well there.

According to Tran, this kind of algorithm is relevant to a wide range of real-world scenarios, including military surveillance, robot collaboration in a warehouse, traffic signal management, delivery coordination by autonomous vehicles, and grid control.

When Seung Hyun Kim was a mechanical engineering undergraduate student, according to Tran, he developed the majority of the theory underlying the concept; Neale Van Stralen, an aerospace undergraduate, assisted with the implementation. Both students received guidance from Tran and Girish Chowdhary. At the peer-reviewed conference on autonomous agents and multi-agent systems, the work was recently presented to the AI community.

University of Illinois Grainger College of Engineering

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