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PowerPoint Presentation Model-Based Policy Learning CS 285: Deep Reinforcement Learning, Decision Making, and Control Sergey Levine Class Notes 1. Homework 3 is out! Due next week • Start early, this one will take a bit longer! 1. Last time: model-based reinforcement learning without policies 2. Today: model-based reinforcement learning of policies • Learning global policies • Learning local polic
PowerPoint Presentation Reframing Control as an Inference Problem CS 285: Deep Reinforcement Learning, Decision Making, and Control Sergey Levine Class Notes 1. Homework 3 is out! Due Oct 21 • Start early, this one will take a bit longer! Today’s Lecture 1. Does reinforcement learning and optimal control provide a reasonable model of human behavior? 2. Is there a better explanation? 3. Can we deri
PowerPoint Presentation Inverse Reinforcement Learning CS 285: Deep Reinforcement Learning, Decision Making, and Control Sergey Levine Today’s Lecture 1. So far: manually design reward function to define a task 2. What if we want to learn the reward function from observing an expert, and then use reinforcement learning? 3. Apply approximate optimality model from last week, but now learn the reward
Study Circle in Deep Reinforcement Learning Lecture 0 Gautham Nayak Seetanadi Dept. of Automatic Control, Lund Institute of Technology February 9, 2021 Study Circle I We will follow online courses and assignments I The topics might change over time I Happy for input or suggestions for the course I Current course ends Mid-April. Might speed up at the end I Active participation in course for credits
Deep RL Assignment 1: Imitation Learning Fall 2019 due September 16th, 11:59 pm The goal of this assignment is to experiment with imitation learning, including direct behavior cloning and the DAgger algorithm. In lieu of a human demonstrator, demonstrations will be provided via an expert policy that we have trained for you. Your goals will be to set up behavior cloning and DAgger, and compare thei
CS285 Deep Reinforcement Learning HW3: Q-Learning and Actor-Critic Due: October 21st 2019, 11:59 pm 1 Part 1: Q-Learning 1.1 Introduction Part 1 of this assignment requires you to implement and evaluate Q-learning with convolutional neural networks for playing Atari games. The Q-learning algorithm was covered in lecture, and you will be provided with starter code. A GPU machine will be faster, but
https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/StudyCircleDeepReinforcementLearning/hw3.pdf - 2025-11-18
Study Circle in Reinforcement Learning Study Circle in Reinforcement Learning Coordinator: Karl-Erik Årzén Study Circle • A study circle and not a course • I know probably much less about RL than you do • Active participation Lectures and Meetings • The University College London (UCL) course ”Reinforcement Learning” by David Silver • 10 Video Lectures • Accompanying slides • Exercises • Code • Mee
https://www.control.lth.se/fileadmin/control/Education/DoctorateProgram/StudyCircleReinforcementLearning/Notes1.pdf - 2025-11-18
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REGLERTEKNIK LTH KURSINFORMATION NETWORK DYNAMICS FRTN30 (VT LP2, 7.5 hp) Why studying network dynamics? Networks permeate our modern societies. Everyday, we exchange information through the World Wide Web and other comminucation networks, modify our opinions and take decisions under the influence of our social interactions, commute across road networks, buy goods made available to us by productio
https://www.control.lth.se/fileadmin/control/Education/EngineeringProgram/FRTN30/FRTN30NetworkDynamics.pdf - 2025-11-18
19 August 2014 English version - November 2014 Instructions for a written critical review on degree projects at LTH The critical review (a peer review) is to be both oral and written and both parts are to be assessed by the examiner of the student defending the project (the respondent). The reviewer is responsible for providing a written review of the degree project to both the respondent and the
Microsoft Word - Teknisk rapportskrivning.doc In d u st ri a l E le c tr ic a l E n g in e e ri n g a n d A u to m a ti o n Teknisk rapportskrivning Gertrud Pettersson (Nordiska Språk) Gustaf Olsson (IEA) Mats Alaküla (IEA) Dept. of Industrial Electrical Engineering and Automation Lund University Innehåll Förord 2 1 Inledning 3 1.1 Syftet med denna skrift 3 1.2 Olika slag av skrivande 3 1.3 Hur ås
https://www.control.lth.se/fileadmin/control/Education/EngineeringProgram/FRTN45/2018/Teknisk_rapportskrivning.pdf - 2025-11-18
LTH:are spred kunskap om vikten av förebilder - LTH, Lunds Tekniska Högskola 2024-10-09, 15:52LTH:are spred kunskap om vikten av förebilder - LTH, Lunds Tekniska Högskola Page 1 of 4https://lthin.lth.lu.se/nyheter/lth-gemensamt/2024-10-09-lthare-spred-kunskap-om-vikten-av-forebilder.html LTH:are spred kunskap om vikten av förebilder Nyligen var Eva Westin, administrativ chef på Institutionen för r
https://www.control.lth.se/fileadmin/control/External_Engagement/LTHnews-20241009.pdf - 2025-11-18
Irmgard Flügge-Lotz (1850-1891) Sofya Kovalevskaya I began to feel an attraction for my mathematics so intense that I started to neglect my other studies. 1 Russian mathematician and one of the first women to become a professor of mathematics Best known for: The Cauchy-Kovalevskaya theorem, a cornerstone in the theory of partial differential equations, and the Kovalevskaya top, a classic example o
https://www.control.lth.se/fileadmin/control/External_Engagement/Presentation_HistoricalFemaleInfluencers_250612.pptx - 2025-11-18
Industry Club Day 2023 March.pdf
https://www.control.lth.se/fileadmin/control/IndustryClub/Industry_Club_Day_-_March_23__2023.pdf - 2025-11-18
Untitled JitterTime 1.2—Reference Manual Anton Cervin Department of Automatic Control Technical Report TFRT-7658, version 3 ISSN 0280–5316 Department of Automatic Control Lund University Box 118 SE-221 00 LUND Sweden © 2020 by Anton Cervin. All rights reserved. Printed in Sweden. Lund 2020 Abstract This technical report describes JITTERTIME, a Matlab toolbox for calculating the time-varying state
https://www.control.lth.se/fileadmin/control/Research/JitterTime/report1_2.pdf - 2025-11-18
manual.dvi MPCtools 1.0 — Reference Manual Johan Åkesson Department of Automatic Control Lund Institute of Technology January 2006 Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2. Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3. Model Predictive Control – Background . . . . . . . . . . . . . 6 4. Linear Model Predictive Contr
https://www.control.lth.se/fileadmin/control/Research/MPCtools/MPCtools-1.0-manual.pdf - 2025-11-18
Examples QPgen Home Examples Installation Documentation Licence Authors Citing Examples Here we show through two examples how to generate code using QPgen. We also present some performance results. Model predictive control Model predictive control of the pitch angle in an aircraft. Code that generates MPC controller Simulation code Problem size: 60 decision variables 40 equality constraints 80 con
https://www.control.lth.se/fileadmin/control/Research/Tools/qpgen/examples.html - 2025-11-18
The \(h\) function QPgen Home Examples Installation Documentation Licence Authors Citing The \(h\) function The function \(h : \mathbf{R}^p\to\mathbf{R}\cup\{\infty\}\) is separable down to the component and is piecewise linear in each component. The figure shows a 1-dimensional example. The function can model, e.g. ,: Hard constraints (if slopes are infinite) Soft constraints (if finite slope) Th
https://www.control.lth.se/fileadmin/control/Research/Tools/qpgen/hfcn.html - 2025-11-18
Licence QPgen Home Examples Installation Documentation Licence Authors Citing Licence QPgen and its generated code is licenced under the GNU Affero GPL v3 licence. By using QPgen and/or its generated code, you agree to the terms in the licence. If this licence is not suitable for your purposes, please contact the authors for a different licence agreement. Page generated 2015-12-09 10:07:10 CET, by
https://www.control.lth.se/fileadmin/control/Research/Tools/qpgen/licence.html - 2025-11-18
1407MTNS.pdf Scalable Analysis and Control of Positive Systems Anders Rantzer LCCC Linnaeus Center Lund University Sweden Anders Rantzer, LCCC Linnaeus center Scalable Analysis and Control of Positive Systems Wind Farms Need Control Picture from http://www.hochtief.com/hochtief_en/9164.jhtml Most wind farms today are paid to maximize power production. Future farms will have to curtial power at con
https://www.control.lth.se/fileadmin/control/staff/AndersRantzer/1407MTNS_4slides.pdf - 2025-11-18
