Continuous Control with a Combination of Supervised and Reinforcement Learning

Total Page:16

File Type:pdf, Size:1020Kb

Continuous Control with a Combination of Supervised and Reinforcement Learning ORE Open Research Exeter TITLE Continuous Control with a Combination of Supervised and Reinforcement Learning AUTHORS Kangin, D; Pugeault, N JOURNAL Proceedings of the International Joint Conference on Neural Networks DEPOSITED IN ORE 23 April 2018 This version available at http://hdl.handle.net/10871/32566 COPYRIGHT AND REUSE Open Research Exeter makes this work available in accordance with publisher policies. A NOTE ON VERSIONS The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date of publication Continuous Control with a Combination of Supervised and Reinforcement Learning Dmitry Kangin and Nicolas Pugeault Computer Science Department, University of Exeter Exeter EX4 4QF, UK fd.kangin, [email protected] Abstract—Reinforcement learning methods have recently sures: for example, by the average speed, time for completing achieved impressive results on a wide range of control problems. a lap in a race, or other appropriate criteria. This situation is However, especially with complex inputs, they still require an the same for other control problems connected with robotics, extensive amount of training data in order to converge to a meaningful solution. This limits their applicability to complex including walking [9] and balancing [10] robots, as well as in input spaces such as video signals, and makes them impractical many others [11]. In these problems, also usually exist some for use in complex real world problems, including many of those criteria for assessment (for example, time spent to pass the for video based control. Supervised learning, on the contrary, is challenge), which would help to assess how desirable these capable of learning on a relatively limited number of samples, control actions are. but relies on arbitrary hand-labelling of data rather than task- derived reward functions, and hence do not yield independent The problem becomes even more challenging if the results control policies. In this article we propose a novel, model- are dependent on the sequence of previous observations [12], free approach, which uses a combination of reinforcement and e.g. because of dynamic nature of the problem involving speed supervised learning for autonomous control and paves the way or acceleration, or the difference between the current and the towards policy based control in real world environments. We previous control signal. use SpeedDreams/TORCS video game to demonstrate that our approach requires much less samples (hundreds of thousands In many real world problems, it is possible to combine against millions or tens of millions) comparing to the state-of-the- reinforcement and supervised learning. For the problem of art reinforcement learning techniques on similar data, and at the autonomous driving, it is often possible to provide parallel same time overcomes both supervised and reinforcement learning signals of the autopilot which could be used to restrict the approaches in terms of quality. Additionally, we demonstrate reinforcement learning solutions towards the sensible subsets applicability of the method to MuJoCo control problems. of control actions. Similar things can also be done for robotic control. Such real world models can be analytical, or trained I. INTRODUCTION by machine learning techniques, and may use some other Recently published reinforcement learning approaches [1], sensors, which are capable to provide alternative information [2], [3] have achieved significant improvements in solving which enables to build the reference model (e.g., the model complex control problems such as vision-based control. They trained on LiDAR data can be used to train the vision based can define control policies from scratch, without mimicking model). However, although there were some works using any other control signals. Instead, reward functions are used to partially labelled datasets within the reinforcement learning formulate criteria for control behaviour optimisation. However, framework [13], as far as we believe, the proposed prob- the problem with these methods is that in order to explore the lem statement, injecting supervised data into reinforcement control space and find the appropriate actions, even the most learning using regularisation of Q-functions, is different from recent state-of-the-art methods require several tens of millions the ones published before. In [13], the authors consider the of steps before convergence [1], [4]. problem of robotic control which does not involve video data, One of the first attempts to combine reinforcement and and their approach considers sharing the replay buffer between supervised learning can be attributed to [5]. Recently proposed the reinforcement learning and demonstrator data. In [14], the supervised methods for continuous control problems, like [6], authors address the problem of sample efficiency improvement are known to rapidly converge, however they do not provide by using pretraining and state dynamics prediction. However, independent policies. Contrary to many classification or re- the proposed idea of pretraining is different from the one gression problems, where there exists a definitive ground truth, proposed here as the random positions and actions are used. as it is presented in [7], [8], for many control problems an Also, in contrast to the proposed method, which uses image infinitely large number of control policies may be appropriate. input, the approach, proposed in [14], has only been tested on In autonomous driving problems, for example, there is no low-dimensional problems. single correct steering angle for every moment of time as The novelty of the approach, presented in this paper, is given different trajectories can be appropriate. However, a whole as follows: control sequence can be assessed according to objective mea- 1) a new regularised formulation optimisation, combining l Annotated Dataset Supervised Pre-training Initial Model Environment l > 0 : hz1; z2; : : : ; zli 2 Z . We define an operator from the subsequences of real valued quantities to d-dimensional Rewards Control Signals p d control signals π(zi; zi−1; : : : ; zi−p+1jΘπ): Z ! R , Reinforcement&Supervised Learning where i 2 [p; l], p 2 N is the number of frames used to produce the control signal, and Θπ are the parameters of the operator π(·). We denote ci = π(zi; : : : ; zi−p+1jΘπ), Finetuned Model xi = (zi; zi−1; : : : ; zi−p+1). The problem is stated as follows: given the set of N N Fig. 1: The overall scheme of the proposed method j lj sequences z^ 2 Z j=1 and the set of corresponding signal N lj D j dE sequences c^i 2 R , produced by some external reinforcement and supervised learning, is proposed; i=1 j=1 2) a training algorithm is formulated based on this problem control method called a ’reference actor’, find the parameters statement, and assessed on control problems; Θπ of the actor π(·|Θπ), which minimise a loss function (the 3) the novel greedy actor-critic reinforcement learning al- used loss function is defined in Formula (7)). gorithm is proposed as a part of the training algorithm, B. Label-assisted reinforcement learning containing interlaced data collection, critic and actor update stages. The reinforcement learning method is inspired by the DDPG The proposed method reduces the number of samples from algorithm [3], however, it is substantially reworked in order to millions or tens of millions, required to train the reinforcement meet the needs of the proposed combination of supervised and learning model on visual data, to just hundreds of thousands, reinforcement learning working in real time. First, the problem and is shown to converge to policies outperforming the ones statement and basic definitions, necessary for formalisation of achieved by both supervised and reinforcement learning ap- the method, need to be given. proaches. This approach helps to build upon the performance In line with the standard terminology for reinforcement of supervised learning by using reinforcement learning, which learning, we refer to a model, generating control signals, as both provides the ability to learn faster than reinforcement an agent, and to control signals as actions. Also, as it is learning and at the same time to improve policy using rewards, usually done for reinforcement learning problem statements, which is not possible when using just supervised training we assume the states to be equivalent to the observations. algorithms. Initially, the model receives an initial state of the environment x1. Then it applies the action c1, which results in transition ROPOSED METHOD II. P into the state x2. The procedure repeats recurrently, resulting n The overall idea of the method is shown in Fig. 1 and can in sequences of states X = hx1; x2; : : : ; xni and actions n be described as follows: to perform an initial approximation C = hc1; c2; : : : ; cni. Every time the agents performs an by supervised learning and then, using both explicit labels and action it receives a reward r(xi; ci) 2 R [3]. rewards, to fine-tune it. The emitted actions are defined by the policy π, mapping For supervised pre-training, the annotated dataset should states into actions. In general [15], it can be stochastic, contain recorded examples of control by some existing model. defining a probability distribution over the action space, how- The aim of this stage is to mimic the existing control methods ever here the deterministic policy is assumed. For such a in order to avoid control behaviour of the trained model deterministic policy, one can express the discounted future resulting in small rewards. reward using the following form of the Bellman equation for For supervised and reinforcement learning based fine- the expectation of discounted future reward Q(·; ·) [3]: tuning, a pretrained model is used as an initial approximation. Q(x ; c ) = [r(x ; c ) + γQ(x ; π(x ))] ; (1) Contrary to the standard reinforcement learning approaches, i i Exi+1 i i i+1 i+1 the pre-trained model helps it to avoid control values resulting where π(·) is an operator from states to actions, γ 2 [0; 1] is in small rewards right from the beginning.
Recommended publications
  • Ubuntu Unleashed 2013 Edition: Covering 12.10 and 13.04
    Matthew Helmke with Andrew Hudson and Paul Hudson Ubuntu UNLEASHED 2013 Edition 800 East 96th Street, Indianapolis, Indiana 46240 USA Ubuntu Unleashed 2013 Edition Editor-in-Chief Copyright © 2013 by Pearson Education, Inc. Mark Taub All rights reserved. No part of this book shall be reproduced, stored in a retrieval Acquisitions Editor system, or transmitted by any means, electronic, mechanical, photocopying, record- Debra Williams ing, or otherwise, without written permission from the publisher. No patent liability is assumed with respect to the use of the information contained herein. Although every Cauley precaution has been taken in the preparation of this book, the publisher and author Development Editor assume no responsibility for errors or omissions. Nor is any liability assumed for damages resulting from the use of the information contained herein. Michael Thurston ISBN-13: 978-0-672-33624-9 Managing Editor ISBN-10: 0-672-33624-3 Kristy Hart Project Editor The Library of Congress cataloging-in-publication data is on file. Jovana Shirley Printed in the United States of America Copy Editor First Printing December 2012 Charlotte Kughen Trademarks Indexer All terms mentioned in this book that are known to be trademarks or service marks have Angie Martin been appropriately capitalized. Sams Publishing cannot attest to the accuracy of this information. Use of a term in this book should not be regarded as affecting the validity Proofreader of any trademark or service mark. Language Logistics Warning and Disclaimer Technical Editors Every effort has been made to make this book as complete and as accurate as Chris Johnston possible, but no warranty or fitness is implied.
    [Show full text]
  • Openbsd Gaming Resource
    OPENBSD GAMING RESOURCE A continually updated resource for playing video games on OpenBSD. Mr. Satterly Updated August 7, 2021 P11U17A3B8 III Title: OpenBSD Gaming Resource Author: Mr. Satterly Publisher: Mr. Satterly Date: Updated August 7, 2021 Copyright: Creative Commons Zero 1.0 Universal Email: [email protected] Website: https://MrSatterly.com/ Contents 1 Introduction1 2 Ways to play the games2 2.1 Base system........................ 2 2.2 Ports/Editors........................ 3 2.3 Ports/Emulators...................... 3 Arcade emulation..................... 4 Computer emulation................... 4 Game console emulation................. 4 Operating system emulation .............. 7 2.4 Ports/Games........................ 8 Game engines....................... 8 Interactive fiction..................... 9 2.5 Ports/Math......................... 10 2.6 Ports/Net.......................... 10 2.7 Ports/Shells ........................ 12 2.8 Ports/WWW ........................ 12 3 Notable games 14 3.1 Free games ........................ 14 A-I.............................. 14 J-R.............................. 22 S-Z.............................. 26 3.2 Non-free games...................... 31 4 Getting the games 33 4.1 Games............................ 33 5 Former ways to play games 37 6 What next? 38 Appendices 39 A Clones, models, and variants 39 Index 51 IV 1 Introduction I use this document to help organize my thoughts, files, and links on how to play games on OpenBSD. It helps me to remember what I have gone through while finding new games. The biggest reason to read or at least skim this document is because how can you search for something you do not know exists? I will show you ways to play games, what free and non-free games are available, and give links to help you get started on downloading them.
    [Show full text]
  • Secure and Private Sensing for Driver Authentication and Transportation Safety
    University Transportation Research Center - Region 2 Final Report Secure and Private Sensing for Driver Authentication and Transportation Safety Performing Organization: New York Institute of Technology August 2017 Sponsor: University Transportation Research Center - Region 2 University Transportation Research Center - Region 2 Project No(s): 49198-33-27 The Region 2 University Transportation Research Center (UTRC) is one of ten original University Transportation Centers established in 1987 by the U.S. Congress. These Centers were established UTRC/RF Grant No: with the recognition that transportation plays a key role in the nation's economy and the quality Project Date: of life of its citizens. University faculty members provide a critical link in resolving our national and regional transportation problems while training the professionals who address our transpor- Project Title: August 2017 tation systems and their customers on a daily basis. Authentication and Transportation Safety Secure and Private Sensing for Driver The UTRC was established in order to support research, education and the transfer of technology Project’s Website: in the �ield of transportation. The theme of the Center is "Planning and Managing Regional - Transportation Systems in a Changing World." Presently, under the direction of Dr. Camille Kamga, private-sensing-driver-authentication the UTRC represents USDOT Region II, including New York, New Jersey, Puerto Rico and the U.S. http://www.utrc2.org/research/projects/secure-and Virgin Islands. Functioning as a consortium of twelve major Universities throughout the region, UTRC is located at the CUNY Institute for Transportation Systems at The City College of New York, Principal Investigator(s): theme.the lead UTRC’s institution three of main the goalsconsortium.
    [Show full text]
  • Adaptação De Um Jogo Open Source Para O Desenvolvimento De Um Simulador De Trânsito1
    Modalidade do trabalho: Relatório técnico-científico Evento: XXIII Seminário de Iniciação Científica ADAPTAÇÃO DE UM JOGO OPEN SOURCE PARA O DESENVOLVIMENTO DE UM SIMULADOR DE TRÂNSITO1 Henrique Augusto Richter2, Rafael H. Bandeira3, Eldair F. Dornelles4, Rogério S. De M. Martins5, Nelson A. Toniazzo6. 1 Projeto de Iniciação Científica 2 Aluno do Curso de Graduação em Ciência da Computação da UNIJUÍ, bolsista PIBIC/UNIJUÍ, [email protected] 3 Aluno do Curso de Graduação em Engenharia Elétrica da UNIJUÍ, [email protected] 4 Aluno do Curso de Graduação em Ciência da Computação da UNIJUÍ, [email protected] 5 Professor Orientador, Mestre em Computação Aplicada, Curso de Ciência da Computação, [email protected] 6 Professor Orientador, Coordenador do Projeto de Extensão: A Física na Educação para o Trânsito, [email protected] Introdução Segundo dados estatísticos obtidos pelo Departamento Autônomo de Estradas de Rodagem (DAER) que é responsável pela gestão e fiscalização do transporte rodoviário no estado do Rio Grande do Sul, o condutor é identificado como o maior causador dos acidentes de trânsito. Entre os anos de 2010 a 2012, os índices de acidentes ocasionados por motoristas foi muito superior a outras causas como rodovias e veículos em más condições. No ano de 2012, por exemplo, 90,15% dos acidentes ocorridos foram por comportamento inadequado do motorista, em um total de 12868 acidentes ocorridos no Rio Grande do Sul. A Figura 1 exibe a quantidade de acidentes de 2010 a 2012 de acordo com a causa (MASIERO). Modalidade do trabalho: Relatório técnico-científico Evento: XXIII Seminário de Iniciação Científica Figura 1.
    [Show full text]
  • Ubuntu: Unleashed 2017 Edition
    Matthew Helmke with Andrew Hudson and Paul Hudson Ubuntu UNLEASHED 2017 Edition 800 East 96th Street, Indianapolis, Indiana 46240 USA Ubuntu Unleashed 2017 Edition Editor-in-Chief Copyright © 2017 by Pearson Education, Inc. Mark Taub All rights reserved. Printed in the United States of America. This publication is protected Acquisitions Editor by copyright, and permission must be obtained from the publisher prior to any prohib- Debra Williams ited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. For information Cauley regarding permissions, request forms and the appropriate contacts within the Pearson Managing Editor Education Global Rights & Permissions Department, please visit www.pearsoned.com/ permissions/. Sandra Schroeder Many of the designations used by manufacturers and sellers to distinguish their Project Editor products are claimed as trademarks. Where those designations appear in this book, and Lori Lyons the publisher was aware of a trademark claim, the designations have been printed with initial capital letters or in all capitals. Production Manager The author and publisher have taken care in the preparation of this book, but make Dhayanidhi no expressed or implied warranty of any kind and assume no responsibility for errors or omissions. No liability is assumed for incidental or consequential damages in Proofreader connection with or arising out of the use of the information or programs contained Sasirekha herein. Technical Editor For information about buying this title in bulk quantities, or for special sales opportunities (which may include electronic versions; custom cover designs; and content José Antonio Rey particular to your business, training goals, marketing focus, or branding interests), Editorial Assistant please contact our corporate sales department at [email protected] or (800) 382-3419.
    [Show full text]
  • Op E N So U R C E Yea R B O O K 2 0
    OPEN SOURCE YEARBOOK 2016 ..... ........ .... ... .. .... .. .. ... .. OPENSOURCE.COM Opensource.com publishes stories about creating, adopting, and sharing open source solutions. Visit Opensource.com to learn more about how the open source way is improving technologies, education, business, government, health, law, entertainment, humanitarian efforts, and more. Submit a story idea: https://opensource.com/story Email us: [email protected] Chat with us in Freenode IRC: #opensource.com . OPEN SOURCE YEARBOOK 2016 . OPENSOURCE.COM 3 ...... ........ .. .. .. ... .... AUTOGRAPHS . ... .. .... .. .. ... .. ........ ...... ........ .. .. .. ... .... AUTOGRAPHS . ... .. .... .. .. ... .. ........ OPENSOURCE.COM...... ........ .. .. .. ... .... ........ WRITE FOR US ..... .. .. .. ... .... 7 big reasons to contribute to Opensource.com: Career benefits: “I probably would not have gotten my most recent job if it had not been for my articles on 1 Opensource.com.” Raise awareness: “The platform and publicity that is available through Opensource.com is extremely 2 valuable.” Grow your network: “I met a lot of interesting people after that, boosted my blog stats immediately, and 3 even got some business offers!” Contribute back to open source communities: “Writing for Opensource.com has allowed me to give 4 back to a community of users and developers from whom I have truly benefited for many years.” Receive free, professional editing services: “The team helps me, through feedback, on improving my 5 writing skills.” We’re loveable: “I love the Opensource.com team. I have known some of them for years and they are 6 good people.” 7 Writing for us is easy: “I couldn't have been more pleased with my writing experience.” Email us to learn more or to share your feedback about writing for us: https://opensource.com/story Visit our Participate page to more about joining in the Opensource.com community: https://opensource.com/participate Find our editorial team, moderators, authors, and readers on Freenode IRC at #opensource.com: https://opensource.com/irc .
    [Show full text]
  • E-Dergi Pardus Ve Grafik: Fotoğrafları Onarmak
    sayı 21 - Mayıs 2010 özgürlükiçin.com e-dergi Pardus ve Grafik: Fotoğrafları Onarmak Kotalı oyuncular yaşadı: Assault Cube Ofis paketimiz hızlanıyor: OpenOffice.org 3.2 Röportaj: Semen CİRİT Gizli Kahramanlar: Pardus Test Takımı içindekiler künye Bu sayının editörü: Fahri DÖNMEZ 03. Editörden 04-09. Haberler Bu sayıda katkıda bulunanlar: 10-13. Nasıl: Bugzilla’yı kullanmak: Hatamla sev beni Ahmet Hiçyılmaz, Ali Işıngör, Ali Rasim Koçal, Anıl Özbek, 14-16. Kurulum Cd’lerinizi Kontrol Edin Ayhan Yalçınsoy, Ceyhun Alyeşil, 17-20. Python’da Döngüler Deniz Ege Tunçay, Ertan Güven, Gaye Demirbaş, Gizem Belen, 21-24. Frescobaldi ile İşler Nasıl Gidiyor? Göktuğ Korkmaz, Hakan Hamurcu, Hüseyin Sarıgül, Ömer Taban, 25-26. Pardus ve Grafik: Yıpranmış Fotoğrafları Onarmak Razık Hilenoğlu, Semen Cirit, Server Acim, Taha Doğan Güneş 27-32. OpenOffice.org 3.2: Yeni özellikler ve Tuğsan Ünlü. 33-35. Plasma: Plasma’nın Gizli Yüzü-2: Plasma pratiklik kazandırsın Tasarım: 36-38. Paket Tanıtımı: Web tasarımcıları için: Mavi Balık artistanbul (Deniz Ege Tunçay) 39-41. Oyun İnceleme: Assault Cube Özgürlükİçin e-dergisi, 42-46. Test Süreçleri: Pardus’un Gizli Kahramanları: “Test Takımı” Creative Commons 47-48. QA ve Linux Dağıtımları (by-nc-sa) 3.0 ile lisanslanmıştır. 49-52. Pardus Test Süreçleri ve İyileştirmeler Pardus ismi ve logosu, TÜBİTAK UEKAE’nin tescilli markasıdır 53-58. Röportaj: Pardus Test Takımı Yöneticisi: Semen CİRİT Bu yayın, Özgürlükİçin topluluğu tarafından 59. Son Sayfa hazırlanmaktadır. 02 editörden Fahri DÖNMEZ [email protected] Pardus’un bizden beklentileri... Merhaba, özgür yazılım takipçileri! Anladığım kadarıyla destekçiler ikiye ayrılıyor: Katkıcı ve geliştirici. Geliştirici olabilmek için özgür yazılım geliştirme araçlarını iyi Her sayısı heyecanla beklenen ve içeriği Özgürlükİçin topluluğu bilmek ya da öğrenmek gerekiyordu.
    [Show full text]
  • Unknown Horizons Speed Dreams Metro: Last Light Supertuxkart Flightgear
    Linux Games [Brasil] ANO 1 - N#1 - Dezembro 2013 Unknown Horizons Excelente estratégia em tempo real! SuperTuxKart FlightGear Melhor jogo de corrida com o mascote do Linux Melhor simulador de vôo gratuito Speed Dreams Metro: Last Light Corrida com carros envenenados Jogo de tiro denso e perturbador - netPanzer - Neverball - Frozen Bubble - Capitán Sevilla - OpenTTD - OpenTyrian Red Eclipse Warsow Cube 2 - MegaGlest - Battle for Wesnoth Editorial REVISTA LINUX GAMES [BRASIL] ??? Bem vindo a primeira edição da revista Linux Games [Brasil]. Essa é uma revista dedicada exclusivamente a divulgação de jogos disponíveis para a plataforma GNU/Linux (denominado aqui na revista simplesmetne como Linux), que se encontram nos mais vários cantos da internet e sistemas de distribuição como o Steam. Espero poder contar com o apoio de todos na criação dessa, que talvez seja a única revista digital em língua portuguesa de todo mundo, dedicada exclusivamente aos jogos para a plataforma Linux. Devo ressaltar que essa revista não é exclusiva de nenhuma comunidade, nem de uma distribuição específica, é uma revista feita por um usuário do Linux para todos os usuários, fãs e pessoas que querem saber um pouco mais sobre o Linux. Em especial, sobre os jogos disponíveis para a plataforma do pinguim! ;-) Serão abordardos jogos dos mais variados temas, ação, plataforma, survival-horror, fps, shooter, que rodam de maneira nativa na plataforma Linux, ou seja, feitos para Linux, bem como jogos que funcionem perfeitametne no Linux através de outros sistemas (como Wine, DOSBox, etc) e emuladores (Atari, SNES, ScummVM, etc), desde lançamentos, reviews, previews, dicas, tutoriais, e-mails e tudo mais que for interesante para os fãs de jogos e Linux..
    [Show full text]
  • An Interactive Track Generator for TORCS and Speed-Dreams
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Archivio istituzionale della ricerca - Politecnico di Milano TrackGen: An Interactive Track Generator for TORCS and Speed-Dreams Luigi Cardamone, Pier Luca Lanzi, and Daniele Loiacono Dipartimento di Elettronica, Informazione e Bioinformatica Politecnico di Milano, Milano, Italy. Abstract TrackGen is an on-line tool for the generation of tracks for two open-source 3D car racing games (TORCS and Speed Dreams). It integrates interactive evolution with procedural content generation and comprises two components: (i) a web frontend that maintains the database of all the evolved popula- tions and manages the interaction with users (by collecting users evaluations and providing access to the evolved tracks); (ii) an evolutionary/content- generation backend that runs both the evolutionary algorithm and generates the actual game content that is available through the web frontend. The first prototype of the tool was presented in July 2011 but advertised only to researchers; the first official version which generated tracks only for TORCS was released to the game community in September 2011; due to the many requests, we released a new version soon afterwards, in January 2012, with support for Speed Dreams (the fork of TORCS focused on visual realism and graphic quality) that has been on-line since then. From January 2012 until July 2014, TrackGen had more than 7600 unique visitors who visited the web- site around 11500 times and viewed 85500 pages; it was employed to evolve more than 8853 tracks, and it was used to download 12218 tracks.
    [Show full text]
  • Building Linux Distribution Packages with Docker
    Building Linux distribution packages with Docker Bruno Cornec HPE EMEA EG Presales Strategist WW Linux Community Lead, HPE Open Source Pro ession !"#0 – October 20'( #$#A Custo%ers Solution Inno&ation Center Grenoble Ma)ing the ne+ style o ,T a reality # o » './ years o success, +orld +ide programs, including Cloud Center o Excellence, C Big Data Center o Excellence, Open Source Solutions ,nitiati!e, 0,SC to HP Intel Architecture Migrations, N ! Center o Excellence, EMEA Networking Customer 1isit Center and more » C Complete ,- 23$$/ systems, 4$$$/ net+ork ports, .$$/ -B storage5 o » Port olio o 3$/ ready to demo solutions +it* access to our ecosystem o Partners P » Complete test 6 !alidation en!ironment » Strategic partners*ip +it* Intel, '.7year long standing colla&oration » Strategic partners*ip +it* "ed Hat 87year colla&oration 2OSS,5 o % » e A uni9ue proo point in t*e industry +it* a pro!en ser!ice o:ering d e & i L Mission: Accelerate t*e adoption o new and inno!ati!e solutions &y creating simple and re+arding end7to7end customer experiences t*at &ene it our customers and partners, in a p o compelling and engaging colla&orative en!ironment. h s k …more information available at http://www.hpintelco.net r o ' Introducing m(sel) ● So t+are engineering and <nices since '=>>; – Mostly Con iguration Management Systems 2CMS5, Build systems, 9uality tools, on multiple commercial <nix systems – ?isco!ered Open Source 6 Linux 2OSL5 6 made irst contri&utions in '==4 – @ull time on OSL since '==5, irst as HP reseller t*en AHP ● Currently; –
    [Show full text]
  • Fully Virtual Rapid ADAS Prototyping Via a Joined Multi-Domain Co-Simulation Ecosystem
    Fully Virtual Rapid ADAS Prototyping via a Joined Multi-domain Co-simulation Ecosystem R´obert Lajos B¨ucs, Pramod Lakshman, Jan Henrik Weinstock, Florian Walbroel, Rainer Leupers and Gerd Ascheid Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Germany Keywords: ADAS Prototyping, Driving Simulators, Virtual Platforms, Multi-domain Simulation, Near Real-time Execution. Abstract: Advanced Driver Assistance Systems (ADAS) have evolved into comprehensive hardware/software applicati- ons with exploding complexity. Various simulation-driven techniques emerged to facilitate their development, e.g., model-based design and driving simulators. However, these approaches are mostly restricted to functional prototyping only. Virtual platform technology is a promising solution to overcome this limitation by accura- tely simulating the entire ADAS hardware/software stack, including functional and non-functional properties. However, all these tools and techniques are limited to their individual simulation environments. To reap their combined benefits, this paper proposes a joined frameworking to facilitate ADAS prototyping via full vir- tualization and whole-system simulation. For this purpose, an advanced automotive-flavor virtual platform was also designed, ensuring detailed, near real-time simulation. The benefits of the approach and the joined frameworks are shown by prototyping two ADAS applications in various system configurations. 1 INTRODUCTION the sheer complexity of the electronic and electri- cal system and
    [Show full text]
  • Realization of a Soft-Real-Time Automotive Simulator with Human Interaction
    POLITECNICO DI MILANO Facoltà di Ingegneria Industriale Corso di Laurea in Ingegneria Meccanica Realization of a soft-real-time automotive simulator with human interaction Relatore: Prof. Umberto CUGINI Co-relatore: Dott. Francesco FERRISE Co-relatore: Ing. Marco GUBITOSA Tesi di Laurea di: Nicolai BENI Matr. 783207 Anno Accademico 2012 - 2013 “The starry heavens above me and the moral law within me” “Il cielo stellato sopra di me, la legge morale dentro di me” Immanuel Kant, 1788 Ringraziamenti Desidero ringraziare il professor Umberto Cugini, mio relatore, e il dottore Francesco Ferrise, mio correlatore presso il Politecnico di Milano, per avermi offerto la possibilità di svolgere il mio lavoro di tesi presso l’azienda LMS® International1. Uno speciale ringraziamento va all’ingegnere Marco Gubitosa, mio relatore presso LMS® International, per avermi seguito e aiutato in questi sei mesi. Infine ringrazio i miei genitori che mi hanno sostenuto durante questo percorso. 1 LMS® International: a Siemens Business leading partner in test and mechatronics simulation in the automotive, aerospace and other manufacturing industries. Index Abstract ........................................................................................................................................... 7 Sommario ........................................................................................................................................ 9 Riassunto ......................................................................................................................................
    [Show full text]