Reinforcement Learning vs. Differentiable Programming

We’ve discussed the idea of differentiable programming, where we incorporate existing programs into deep learning models. But if you’re a researcher building, say, a self-driving car, what does differentiable programming mean in practice? How does it affect the way we express our problem, train our model, curate our dataset, and ultimately the results we achieve?

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