DeepMind's AI Agents Exceed 'Human-Level' Gameplay In Quake III
DeepMind's AI Agents Exceed 'Human-Level' Gameplay In Quake III
Published on July 05, 2018 at 11:40PM
An anonymous reader quotes a report from The Verge: AI agents continue to rack up wins in the video game world. Last week, OpenAI's bots were playing Dota 2; this week, it's Quake III, with a team of researchers from Google's DeepMind subsidiary successfully training agents that can beat humans at a game of capture the flag. DeepMind's researchers used a method of AI training that's also becoming standard: reinforcement learning, which is basically training by trial and error at a huge scale. Agents are given no instructions on how to play the game, but simply compete against themselves until they work out the strategies needed to win. Usually this means one version of the AI agent playing against an identical clone. DeepMind gave extra depth to this formula by training a whole cohort of 30 agents to introduce a "diversity" of play styles. How many games does it take to train an AI this way? Nearly half a million, each lasting five minutes. DeepMind's agents not only learned the basic rules of capture the flag, but strategies like guarding your own flag, camping at your opponent's base, and following teammates around so you can gang up on the enemy. "[T]he bot-only teams were most successful, with a 74 percent win probability," reports The Verge. "This compared to 43 percent probability for average human players, and 52 percent probability for strong human players. So: clearly the AI agents are the better players."
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Published on July 05, 2018 at 11:40PM
An anonymous reader quotes a report from The Verge: AI agents continue to rack up wins in the video game world. Last week, OpenAI's bots were playing Dota 2; this week, it's Quake III, with a team of researchers from Google's DeepMind subsidiary successfully training agents that can beat humans at a game of capture the flag. DeepMind's researchers used a method of AI training that's also becoming standard: reinforcement learning, which is basically training by trial and error at a huge scale. Agents are given no instructions on how to play the game, but simply compete against themselves until they work out the strategies needed to win. Usually this means one version of the AI agent playing against an identical clone. DeepMind gave extra depth to this formula by training a whole cohort of 30 agents to introduce a "diversity" of play styles. How many games does it take to train an AI this way? Nearly half a million, each lasting five minutes. DeepMind's agents not only learned the basic rules of capture the flag, but strategies like guarding your own flag, camping at your opponent's base, and following teammates around so you can gang up on the enemy. "[T]he bot-only teams were most successful, with a 74 percent win probability," reports The Verge. "This compared to 43 percent probability for average human players, and 52 percent probability for strong human players. So: clearly the AI agents are the better players."
Read more of this story at Slashdot.
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DeepMind's AI Agents Exceed 'Human-Level' Gameplay In Quake III
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