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The spectacular success of modern control theory realized by reinforcement learning suggests that many real-world problems can be solved via a new paradigm of simulation or ‘gamification’. Averting climate change, making mass-mobility sustainable through smart city control and large-scale fleet management, building safe self-driving cars, or revolutionizing any real-world logistics problem: With this new paradigm, a simulator (or game) is built that encompasses all salient features of a problem to be solved. Then, a self-learning AI algorithm plays the game until it finds a solution. Hence an entire class of hitherto intractable intelligent control theory problems has become accessible if one can build an expressive enough simulator.

At IARAI, we build such powerful simulators from industrial-scale real-world data. In these we can advance learning strategies or test one-shot / few-shot learning to help solve some of humanity’s most pressing problems.

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