Learning robust controllers that work across many partially observable environments - Robohub

In intelligent systems, applications range from autonomous robotics to predictive maintenance problems. To control these systems, the essential aspects are captured with a model. When we design controllers for these models, we almost always face the same challenge: uncertainty. We’re rarely able to see the whole picture. Sensors are noisy, models of the system are imperfect; the world never behaves exactly as expected.

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