
Learning to Drive by Differentiating Through Scenes - avik-pal
Differentiable Programming has been shown to perform quite well in simple control problem tasks like cartpole, inverted pendulum, etc. Moreover, it has been used in some more challenging areas where the controller is an ODE solver like in case of Trebuchet. All these examples portray the advantages of using a smaller model in DP as compared to the heavily parameterized models in Reinforcement Learning (RL). This work is in a way a step in the same direction. Only now we combine all those things together and make the &quot;differentiable&quot; program even more complicated than a few ODE equations.
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avik-pal
Link to blog post - [https://avik-
pal.github.io/blog/learning_to_drive.html](https://avik-
pal.github.io/blog/learning_to_drive.html)

