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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? This article shows what DP can bring to some simple but classic control problems, where we would normally use Reinforcement Learning (RL). DP-based models not only learn far more effective control strategies than RL, but also train orders of magnitude faster.