WASHINGTON: It’s official: the machines are going to destroy you (if, that is, you’re a professional multiplayer gamer).
A team of programmers at a British artificial intelligence company has designed automated “agents” that taught themselves how to play the seminal first-person shooter Quake III Arena, and became so good they consistently beat human beings.
The work of the researchers from DeepMind, which is owned by Google’s parent company Alphabet, was described in a paper published in Science on Thursday and marks the first time the feat has ever been accomplished.
To be sure, computers have been proving their dominance over humans in one-on-one turn-based games such as chess ever since IBM’s Deep Blue beat Gary Kasparov in 1997.
More recently, a Google AI agent beat the world’s number one Go player in 2017. But the ability to play multiplayer games involving teamwork and interaction in complex environments had remained an insurmountable task.
For the study, the team led by Max Jaderberg worked on a modified version of Quake III Arena, a game that first appeared in 1999 but continues to thrive in competitive gaming tournaments.
The game mode they chose was “Capture the Flag”, which involves working with teammates to grab the opponent team’s flag while safeguarding your own, forcing players to devise complex strategies mixing aggression and defence.
After the agents had been given time to train themselves up, they matched up their prowess against professional games testers.
“Even after 12 hours of practice, the human game testers were only able to win 25 per cent of games against the agent team,” the team wrote.
The agents’ win-loss ratio remained superior even when their reaction times were artificially slowed down to human levels and when their aiming ability was similarly reduced.
The programmers relied on so-called “Reinforcement Learning (RL)” to imbue the agents with their smarts. “Initially, they knew nothing about the world and instead were doing completely random stuff and bouncing about the place,” Jaderberg said.
The agents were taught to reward themselves for capturing the flag, but the team also devised a series of new and innovative methods to push the boundaries of what is possible with RL.