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Shane Barratt proslijedio/la je Tweet
Shane Barratt, Guillermo Angeris, Stephen Boyd : Automatic Repair of Convex Optimization Problems https://arxiv.org/abs/2001.11010 https://arxiv.org/pdf/2001.11010
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Shane Barratt proslijedio/la je Tweet
Malayandi Palan, Shane Barratt, Alex McCauley, Dorsa Sadigh, Vikas Sindhwani, Stephen Boyd : Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint https://arxiv.org/abs/2001.07572 https://arxiv.org/pdf/2001.07572
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California is spending .1% of its budget on transportation... What a joke. http://www.ebudget.ca.gov/
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We provide examples in portfolio optimization ("Tuning a Markowitz policy to maximize utility"), vehicle control ("Tuning a vehicle controller to track curved paths"), and supply-chain management (" Tuning a supply chain policy to maximize profit"). Check them out in the paper!pic.twitter.com/uIQoCnEJZR
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Our (simple) method is able to reach the performance of a (sophisticated) method based on LMIs, introduced by Wang & Boyd (http://web.stanford.edu/~boyd/papers/stoch_ctrl_bnds.html …).pic.twitter.com/KwT7RMdISN
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Next we apply our method to a box-constrained LQR problem, which has no known exact solution. We use the same COCP as the LQR problem except we add the constraint that the input is in a box.pic.twitter.com/Pgkt46TG04
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As for numerical examples, we start by applying our method to the classical LQR problem, where the parameters are the coefficients of an approximate (quadratic) value function. We see that our method is able to recover a policy with the same cost as the LQR solution.pic.twitter.com/9DjGH6KHXe
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Our method relies on recently developed methods that can efficiently evaluate the derivative of the solution of a convex optimization problem with respect to its parameters, namely cvxpylayers (https://github.com/cvxgrp/cvxpylayers …).
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Tuning the parameters in these policies is often done by hand, or by grid search. In this paper we propose a method to automate this process, by adjusting the parameters using an approximate gradient of a performance metric with respect to the parameters.
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Many control policies used in practice are in fact COCPs. Some examples include the linear quadratic regulator (LQR), convex model predictive control (MPC), and convex approximate dynamic programming (ADP) or control-Lyapunov policies.
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We consider the problem of tuning convex optimization control policies (COCPs), which are control policies that compute the input or action by solving a convex optimization problem that depends on the current state and some parameters.pic.twitter.com/rcXAX9AdrZ
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New paper - Learning Convex Optimization Control Policies, w/
@akshaykagrawal,@b_stellato, and Stephen Boyd. Paper: https://arxiv.org/abs/1912.09529Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Had a great time interning at
@LyftLevel5 last summer. Check out this blog post describing my and a few other intern’s experience there!https://medium.com/lyftlevel5/behind-the-scenes-interning-at-level-5-dfe2c1eb2218 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
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Shane Barratt proslijedio/la je Tweet
Our imaginations are being held hostage by the car.pic.twitter.com/C28JzMnm3Q
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To get a lower bound, we first convert the problem into a mixed-integer convex program using the perspective formulation (https://web.stanford.edu/~boyd/papers/pdf/sw_aff_ctrl.pdf …) and relax the integral constraint to get a lower bound. (See the paper for more details.) Here is an example of the lower bound.pic.twitter.com/y4moDiDO4N
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Our algorithm requires solving roughly 5-20 convex optimization problems, and we have implemented it as a CVXPY extension, making it easy to (approximately) solve such problemspic.twitter.com/n3azp19CWs
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And clipped control, where, for example, the cost encourages us to be in one of the two lanespic.twitter.com/Dc5L8iTpkv
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Applications include clipped empirical risk minimization, for example, clipped regressionpic.twitter.com/dT9PbGj3ZE
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