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Rise of the Machines: AI Beats Humans in Multiplayer Shooter
DeepMind, owned by Googlersquo;s parent company Alphabet, has designed automated quot;agentsquot; that taught themselves how to play a competitive first-person multiplayer video game shooter, and became so good they consistently beat human beings.
A team of programmers at a British artificial intelligence company has designed automated agents that taught themselves how to play a competitive first-person multiplayer video game shooter, and became so good they consistently beat human beings
The work of the researchers from DeepMind, which is owned by Googles 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 flexing their dominance over humans in one-on-one turn-based games such as chess ever since IBMs Deep Blue beat Gary Kasparov in 1997 More recently, a GoogleAI agent beat the worlds 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 seminal shooter that was first released in 1999 but continues to thrive in the eSports world
The game mode they chose was Capture the Flag, which involves working with teammates to grab the opponent teams 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, their prowess was matched up against professional games testers
Even after 12 hours of practice, the human game testers were only able to win 25% of games against the agent team, the team wrote, while the agents performance remained superior even when their reaction times were artificially slowed down to human levels
New steps for AI 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 told AFP
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
One of the contributions of the paper is each agent learns its own internal reward signal, said Jaderbeg, meaning that the AI players gave themselves a pat on the back of varying magnitude for accomplishing tasks such as picking up the flag or successfully shooting an opponent
Next, they found that training a population of agents together, rather than one at a time, made the population as a whole learn much faster
They also devised a new architecture of so-called two timescale learning, which Jaderberg likened to the thesis of the book Thinking Fast and Slow
You have one part of the agent which kicks very quickly, it updates its own beliefs very quickly, and you have another part of the agent, which updated belief at a slower rate, and these two beliefs influence each other and help shape the way the agent learns about the world, he said
Finally, randomising the map for each new match was key That meant the solutions that the agents find have to be general - they cannot just memorise a sequence of actions, said co-author Wojciech Czarnecki
Ethics questions The team did not comment, however, on the AIs potential for future use in military settings
DeepMind has publicly stated in the past that it is committed to never working on any military or surveillance projects, and the word shoot does not appear even once in the paper the process is described instead as tagging opponents by pointing a laser gadget at them
Moving forward, Jaderberg said his team would like to explore having the agents play in the full version of Quake III Arena and find ways his AI could work on problems outside of games
We use games, like Capture the Flag, as challenging environments to explore general concepts such as planning, strategy and memory, which we believe are essential to the development of algorithms that can be used to help solve real-world problems
For the latest tech news and reviews, follow Gadgets 360 on Twitter, Facebook, and subscribe to our YouTube channel
Timez Doctor
DeepMind, owned by Googlersquo;s parent company Alphabet, has designed automated quot;agentsquot; that taught themselves how to play a competitive first-person multiplayer video game shooter, and became so good they consistently beat human beings.
A team of programmers at a British artificial intelligence company has designed automated agents that taught themselves how to play a competitive first-person multiplayer video game shooter, and became so good they consistently beat human beings
The work of the researchers from DeepMind, which is owned by Googles 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 flexing their dominance over humans in one-on-one turn-based games such as chess ever since IBMs Deep Blue beat Gary Kasparov in 1997 More recently, a GoogleAI agent beat the worlds 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 seminal shooter that was first released in 1999 but continues to thrive in the eSports world
The game mode they chose was Capture the Flag, which involves working with teammates to grab the opponent teams 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, their prowess was matched up against professional games testers
Even after 12 hours of practice, the human game testers were only able to win 25% of games against the agent team, the team wrote, while the agents performance remained superior even when their reaction times were artificially slowed down to human levels
New steps for AI 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 told AFP
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
One of the contributions of the paper is each agent learns its own internal reward signal, said Jaderbeg, meaning that the AI players gave themselves a pat on the back of varying magnitude for accomplishing tasks such as picking up the flag or successfully shooting an opponent
Next, they found that training a population of agents together, rather than one at a time, made the population as a whole learn much faster
They also devised a new architecture of so-called two timescale learning, which Jaderberg likened to the thesis of the book Thinking Fast and Slow
You have one part of the agent which kicks very quickly, it updates its own beliefs very quickly, and you have another part of the agent, which updated belief at a slower rate, and these two beliefs influence each other and help shape the way the agent learns about the world, he said
Finally, randomising the map for each new match was key That meant the solutions that the agents find have to be general - they cannot just memorise a sequence of actions, said co-author Wojciech Czarnecki
Ethics questions The team did not comment, however, on the AIs potential for future use in military settings
DeepMind has publicly stated in the past that it is committed to never working on any military or surveillance projects, and the word shoot does not appear even once in the paper the process is described instead as tagging opponents by pointing a laser gadget at them
Moving forward, Jaderberg said his team would like to explore having the agents play in the full version of Quake III Arena and find ways his AI could work on problems outside of games
We use games, like Capture the Flag, as challenging environments to explore general concepts such as planning, strategy and memory, which we believe are essential to the development of algorithms that can be used to help solve real-world problems
For the latest tech news and reviews, follow Gadgets 360 on Twitter, Facebook, and subscribe to our YouTube channel
Timez Doctor
- Get link
- X
- Other Apps
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