DeepMind technique encourages AI players to cooperate in zero-sum games

In a preprint paper, DeepMind described a new reinforcement learning technique that models human behavior in a potentially new and powerful way. It could lead to much more capable AI decision-making systems than have been previously released, which could be a boon for enterprises looking to boost productivity through workplace automation.

In “Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games,” DeepMind — the research division of Alphabet whose work chiefly involves reinforcement learning, an area of AI concerned with how software agents ought to take actions to maximize some reward — introduces an economic competition model with a peer-to-peer contract mechanism that enables the discovery and enforcement of alliances among agents in multi-player games. The coauthors say that this sort of alliance formation confers advantages that wouldn’t exist were the agents to go it alone.