One of the key challenges of deep reinforcement learning models—the kind of AI systems that have mastered Go, StarCraft 2, and other games—is their inability to generalize their capabilities beyond their training domain. This limit makes it very hard to apply these systems to real-world settings, where situations are much more complicated and unpredictable than the environments where AI models are trained. But scientists at AI research lab DeepMind claim to have taken the “first steps to train an agent capable of playing many different games without needing human interaction data,” according to a blog post about their new “open-ended…
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