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+Learning convex optimization control policies
+=============================================
+
+This repository accompanies the paper [Learning convex optimization control policies](http://web.stanford.edu/~boyd/papers/pdf/learning_cocps.pdf). It contains the source code for the examples therein.
+
+Many control policies used in various applications determine the input or
+action by solving a convex optimization problem that depends on the current
+state and some parameters. These types of control policies are tuned by varying
+the parameters in the optimization problem, such as the linear quadratic
+regulator weights, to obtain good performance, judged by application-specific
+metrics. Our paper introduces a method to automate this process, by adjusting
+the parameters using an approximate gradient of the performance metric with
+respect to the parameters. Our procedure relies on recently developed methods
+that can efficiently evaluate the derivative of the solution of a convex
+optimization problem with respect to its parameters.
+
+This repository contains several examples of our method, in IPython notebooks.
+Our examples make use the Python package
+[cvxpylayers](https://github.com/cvxgrp/cvxpylayers) to differentiate through
+convex optimization problems.