reinforcement learning: an introduction amazon

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We will be mentored and guided by the AWS experienced representatives from AWS to learn and apply the reinforcement learning … Endorsements Code Solutions Figures Errata/notes CourseMaterials. At some point, there are enough names to overload a lifetime of learning. Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning…. 5 New fromS$94.79. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. This book is the solution, you not only can learn the nitty gritty details of the mathematical justification, all the "whys" but also the "hows", the pseudo code are the parts that I enjoy the most. This is a great book on reinforcement learning. Essential intuitions about Reinforcement Learning, Reviewed in the United States on November 28, 2018. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. After viewing product detail pages, look here to find an easy way to navigate back to pages you are interested in. New and updated guide about Ai, Machines and Algorithms that influence our life. Part I defines the reinforcement learning problem in terms of Markov decision processes. or robotics. --. Html version @MIT Press @Amazon @chapters/indigo These items are shipped from and sold by different sellers. Reinforcement Learning: An Introduction Richard S. Sutton and Andrew G. Barto Second Edition (see here for the first edition) MIT Press, Cambridge, MA, 2018. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning … We teach about some people as if they are extraordinary - as if no one else is working on the same things. Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at any scale.In addition to building ML models using more commonly used supervised and unsupervised learning techniques, you can also build reinforcement learning (RL) models using Amazon … Buy from Amazon Errata and Notes Full Pdf Without Margins Code Solutions-- send in your solutions for a chapter, get the official ones back (currently incomplete) … It's the best book of reinforcement learning. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. Given a pre-specified reward signal, the RL agent interacts with the environment. The book is divided into three parts. Unable to add item to List. Reviewed in the United States on January 30, 2019. Compared to Kindle version, printed version: Reviewed in the United States on February 26, 2019. Richard S. Sutton is Professor of Computing Science and AITF Chair in Reinforcement Learning and Artificial Intelligence at the University of Alberta, and also Distinguished Research Scientist at DeepMind. Reviewed in the United States on November 5, 2004. As a subfield of artificial intelligence, reinforcement learning has shown great success from both a theoretical and practical viewpoint. Ultimate Step by Step Guide to Machine Learning Using Python: Predictive modelling ... An Introduction to Information Theory (Dover Books on Mathematics). It introduces all necessary and relevant algorithms for the beginner. TensorFlow Reinforcement Learning Quick Start Guide: Get up and running with traini... Machine Learning: Master Machine Learning Fundamentals For Beginners. I have taken many courses online about supervised learning but the study material for RL is severely lacking, let alone high quality ones where you can follow and learn this topic in a systematic way. Since that is how the world works, it makes some of the communications easier if we understand this convolution. This Bootcamp is held by Amazon Web Service (AWS) and Jakarta Machine Learning (JML). Reviewed in the United Kingdom on February 19, 2014. This shopping feature will continue to load items when the Enter key is pressed. Let math be just that - math. Unfortunately, it is only done as a means of representing the author's work (which is the intent). Emphasis on basic concepts as sets, the probability measure associated with sets, sample space, random variables, information measure, and capacity. By the time of this post, Sutton also has the complete draft of 2017Nov5 which is also public online, which integrated many of the new progress like deep learning, alphaGo, ..etc. The only necessary mathematical background is familiarity with elementary concepts of probability. I found the monte-carlo sections of this book particularly grueling, but that I think says more about my limits than the content of this book. Your recently viewed items and featured recommendations, Select the department you want to search in. Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series). Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. There was a problem loading your book clubs. Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. The print quality is extremely low. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize … I have read one third so far. Reviewed in the United States on March 28, 2019, Disappointing print with low quality. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Reinforcement Learning – An Introduction (Adaptive Computation and Machine Learning series) Andrew G. Barto is Professor Emeritus in the College of Computer and Information Sciences at the University of Massachusetts Amherst. Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) (English Edition) eBook: Sutton, Richard S., Barto, Andrew G.: Amazon… Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Adaptive…. I like the book. I very appreciate their consideration service. I know that this book is a bible of RL. Updated hourly. No complaints apart from a minor scuff on the cover, upon delivery. An excellent read for anyone looking to learn about Reinforcement Learning. Very easy to read, covers all basic material (and some more advanced) it is actually a very enjoyable book to read if you are in the field of A.I. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. Robotics often involves training complex sequences of behaviors. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. Before you begin, make sure you are able to read Python script and you have some familiarity with Reinforcement Learning concepts. Bring your club to Amazon Book Clubs, start a new book club and invite your friends to join, or find a club that’s right for you for free. Recently, I joined a Bootcamp that will start my journey in learning about reinforcement learning (RL). You're listening to a sample of the Audible audio edition. Everyday low prices and free delivery on eligible orders. I was also familiar with the first edition (1998). It also analyzes reviews to verify trustworthiness. The fundamentals of traditional Logic Programming and the benefits of using the technology to create runnable specifications for complex systems. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. Reinforcement Learning, s... Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Something went wrong. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. Pattern Recognition and Machine Learning (Information Science and Statistics). An introduction to reinforcement learning with AWS RoboMaker. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. Part 1 deals with defining Reinforcement Learning problems in terms of Markov decision processes. And the book's quality is also very good. There was a problem loading your book clubs. I will certainly recommend it to all my students and the many other graduate students and researchers who want to get the appropriate context behind the current excitement for RL.”. Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, and more, Reviewed in the United States on January 28, 2019. The introduction nd building of concepts explain these guys know what they are talking about, Reviewed in the United States on August 23, 2019. It details how refactoring can make things more understandable. I knew the photo of book cover is from the first edition but the page said "second edition", so I decided to order one, hoping to get the second edition. Buy Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) second edition by Sutton, Richard S., Barto, Andrew G., Bach, Francis (ISBN: 9780262039246) from Amazon's Book Store. However, I received the book with low-quality material and broken cover, and poor printing. “The second edition of Reinforcement Learning by Sutton and Barto comes at just the right time. Roll up your sleeves and start programming AI models - no math or data science background required! I would say that a class on Abstract ABC's so to speak would allow us to speak in a more abstract fashion and approach math form its constituent parts when building an algorithm. There's a problem loading this menu right now. Compared to other machine learning materials I have read or watched or listened, this is very well written and they put effort to help you understand. I mean, a Kalman filter is really only a control system feedback loop of a Bayes equation. This item cannot be shipped to your selected delivery location. Everyday low prices and free delivery on eligible orders. Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series). Find all the books, read about the author, and more. One can easily expect spending hundreds of hours to swimming in the details if you want to, thanks to this book, you can also use it as a reference material! Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series): Sutton, Richard S., Barto, Andrew G.: 9780262039246: Amazon.com: Books. Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Given the comments complaining about the poor print quality of the (new) book sold by Amazon, I bought the used one. Details. The spine bends over backwards when I pick up the book. Reviewed in the United Kingdom on August 18, 2019. Taking the form of numerous applications in finance, network engineering, robot toys, and games, it is clear that his learning paradigm shows even greater promise for future developments. Something went wrong. I understand that mathematicians who are publishing new materials must arrive there, I just don't think that the process of getting there needs to be so difficult. Amazon Bestsellers Our most popular products based on sales. If you want to fully understand the fundamentals of learning agents, this is the textbook to go to and get started with. Please try your request again later. Your recently viewed items and featured recommendations, Select the department you want to search in. Bring your club to Amazon Book Clubs, start a new book club and invite your friends to join, or find a club that’s right for you for free. Algorithms: The Humans in the age of AI. These items are shipped from and sold by different sellers. Previous page of related Sponsored Products. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In order to navigate out of this carousel please use your heading shortcut key to navigate to the next or previous heading. To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Please try again. The widely acclaimed work of Sutton and Barto on reinforcement learning applies some essentials of animal learning, in clever ways, to artificial learning systems. Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. The book has a nice ansatz in that it is a comprehensive review of current techniques in reinforcement learning. It's a must have if you're in the field of machine learning. IF we scrapped all of that crap about the names, and worked with names that had logical connections, then the learning would be greatly enhanced. There is enough detail for the reader to be able to implement a decent reinforcement learning algorithm after reading this book. The final chapter discusses the future societal impacts of reinforcement learning. ", Reviewed in the United States on January 26, 2019, An excellent read for anyone looking to learn about Reinforcement Learning, Reviewed in the United States on January 29, 2018. Time for you to start exploring and to expand your skills in Machine Learning with this powerful book for beginners. Unfortunately, now I only have a single option to return with a $0.00 value because this book is a replacement of my original order. Please try your request again later. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. Please try again. Not that there are many books on Reinforcement Learning, but this is probably the best there is. After viewing product detail pages, look here to find an easy way to navigate back to pages you are interested in. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. Book quality is so low, chapter 3 and 4 are repeated twice and only first 7 page of each chapter is in the book. The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. We don’t share your credit card details with third-party sellers, and we don’t sell your information to others. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. I am by no means claiming talent at taking these abstract ideas and presenting them in an easily consumable fashion. A Bradford Book; second edition (March 1, 1998). Reviewed in the United States on February 2, 2019. This necessitates a simpler approach equivalent to the building blocks of atoms. This book is full of those references, but that is at least what I was looking for. Read honest and unbiased product reviews from our users. Read honest and unbiased product reviews from our users. My (now proved wrong) expectation was to receive a used version of the high-quality printed edition (MIT press); the price ($46) was very close to the new book ($50) so I assumed it was not the same printing. Reviewed in the United States on October 15, 2018, Very good introduction and answers the question of "how do I actually represent error in an online learning system where I do not know what the right answer is? The appetite for reinforcement learning among machine learning researchers has never been stronger, as the field has been moving tremendously in the last twenty years. Reviewed in the United States on July 5, 2019. The covers are shorter than the pages. Please choose a different delivery location. Paperback ₹ 1,987.00 #19. I'll probably return this item. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. S$94.79. It's hard going but worth the effort, if you can stand the relentless bellman equations. It also analyzes reviews to verify trustworthiness. Prime members enjoy FREE Delivery and exclusive access to music, movies, TV shows, original audio series, and Kindle books. This is mostly because math is one of the most convoluted areas in education. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. I've already tried "replace order" to fix the book's bad hardcover quality, but looks like that wasn't the only problem. This isn't a simple theory but many of the ideas and methods are practically useful and if you have an interest in neural networks or learning systems then you need to study this book for the six months it deserves!" Reviewed in the United Kingdom on May 6, 2014. We work hard to protect your security and privacy. The pages are not straight. Amazon SageMaker now enables developers and data scientists to quickly and easily develop reinforcement learning models at scale with Amazon SageMaker RL. has been added to your Cart. Please try again. Probably the best and easiest to read book on R.Learning, Reviewed in the United Kingdom on June 29, 2011. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Shantong Zhang who helped replicated all the experiments also have ALL, I mean ALL the experiments implemented in Python, which you can easily find on github. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. This book covers the ground essential to understanding much of the work out their published on RL. Please try again. Hardcover. However, the print quality is extremely low. I read the entire book cover to cover, doing every (non-programming) exercise, as part of a PhD involving RL. It follows with 3 chapters on the 3 fundamental approaches to reinforcement learning: Dynamic programming, Monte Carlo and Temporal Difference methods. Top subscription boxes – right to your door, Adaptive Computation and Machine Learning series, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition…, © 1996-2020, Amazon.com, Inc. or its affiliates. Keep math history as a class called math history. The authors summarize the foundations of reinforcement learning, some of this coming from their own work over the last decade. Its goal is to make actions based on the state it receives, and to maximize the expected cumulative … Then, you learn how the relationship of the pieces affects the whole. For the 2020 holiday season, returnable items shipped between October 1 and December 31 can be returned until January 31, 2021. There's a problem loading this menu right now. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to … Reviewed in the United States on July 13, 2018. The book starts with examples and intuitive introduction and definition of reinforcement learning. Disappointing. The book is divided into 3 parts. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. I like it so much! Sold by Globalmart Online Shop and ships from Amazon Fulfillment. Buy from Amazon Errata and Notes Full Pdf Without Margins Code Solutions-- send in your solutions for a chapter, get the official ones back (currently incomplete) … The spine feels like it's made of cheap cardboard and is not straight not does it cover all the pages. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. It's the first edition (no matter what the page says), Reviewed in the United States on May 17, 2020. Reinforcement learning (RL) is used to automate decision-making in a variety of domains, including games, autoscaling, finance, robotics, recommendations, and supply chain.Launched at AWS re:Invent 2018, Amazon SageMaker RL helps you quickly build, train, and deploy policies learned by RL. It explains things adequately. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. This book has all the "whats", all the "whys" and all the "hows"! Machine Learning for Algorithmic Trading: Predictive models to extract signals from... Hands-On Data Science for Marketing: Improve your marketing strategies with machine... To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. At $57, the quality is utterly unacceptable. The term machine learning is often used interchangeably with artificial intelligence, but in fact these terms refer to related, but separate, concepts. I don't know how this happened but I am returning asap and will try to find an official copy of this book. For example, all figures in color are blurry and faint as if the book was printed with low toners/inks. Richard S. Sutton is Professor of Computing Science and AITF Chair in Reinforcement Learning and Artificial Intelligence at the University of Alberta, and also Distinguished Research Scientist at DeepMind. Naturally, first version was delivered and I'm returning it. Top subscription boxes – right to your door, Adaptive Computation and Machine Learning series, Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration…, © 1996-2020, Amazon.com, Inc. or its affiliates. Reviewed in the United States on February 3, 2018. I think that the field is more difficult than it needs to be. i Reinforcement Learning: An Introduction Second edition, in progress Richard S. Sutton and Andrew G. Barto c 2014, 2015 A Bradford Book The MIT Press Well written and fairly easy to understand even if you're not a math genius. Reinforcement Learning: An Introduction Richard S. Sutton and Andrew G. Barto Second Edition (see here for the first edition) MIT Press, Cambridge, MA, 2018. You're listening to a sample of the Audible audio edition. I always dont write reviews but hands diwn this is it for me.I.love this so much..They only sign to know if people know what they are talking about is to see how they explain the fundamental concepts. Reviewed in the United Kingdom on August 27, 2019, Every single page is lucid, and joy to read. In order to navigate out of this carousel please use your heading shortcut key to navigate to the next or previous heading. Deep Reinforcement Learning with Python: Master classic RL, deep RL, distributional... Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Lear... GANs in Action: Deep learning with Generative Adversarial Networks. It has been extended with modern developments in deep reinforcement learning while extending the scholarly history of the field to modern days. Read honest and unbiased product reviews from our users. Watch a recent AWS Twitch stream to learn more. Machine learning refers to the use of learning algorithms that build a model of understanding about the relationships between existing data to make predictions about new data. Although the goal is easy to describe (the closer the robot is to the object, the better), creating the logic … Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. If you don't know about those people, then this book is either ripe for the reading, or a little bit out of reach. Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. For example, consider a robot designed to follow or track another object. No se especfica que la edicion es la primera y que no tienen todos los capítulos. To get the free app, enter your mobile phone number. This book would barely pass as an EE version of a textbook. Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series), Deep Learning (Adaptive Computation and Machine Learning series), Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition, Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series), Reinforcement Learning and Optimal Control, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). 4.4 out of 5 stars 9. The … Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Our payment security system encrypts your information during transmission. THE introduction to reinforcement learning methods. Find helpful customer reviews and review ratings for Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) at Amazon.com. This is basically the bible of reinforcement learning. Well written, with many examples and a few graphs, and explained mathematical formulas. ... reinforcement learning, deep learning, and artificial intelligence with Python Hadelin de Ponteves. Introduction to reinforcement learning Reinforcement learning develops strategies for sequential decision-making problems. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. I long for the author that is willing to rewrite the formulas, perhaps using artificial intelligence to compile optimal absorption. As it is, when you want to learn the fundamentals of math, you are required to learn math history. Sold by Globalmart Online Shop and ships from Amazon Fulfillment. Part 2nd deals with solutions to dynamic programming and Part 3 incorporates artificial neural networks which are most important while learning Reinforcement Learning. The complexity in RL is transformed to joy of RL, Reviewed in the United Kingdom on October 11, 2020. Discover the main player of Silicon Valley and China. Its a very good book and the sellers are very nice. Until then, anyone learning more about math history than math will never be a leader in math. Explore this example-packed beginner's guide and start your reinforcement and deep reinforcement learning journey with state-of-the-art algorithms, Bradford Books; second edition (November 13, 2018). Reviewed in the United Kingdom on November 24, 2019. In machine learning circles, there is a lot of buzz about reinforcement learning because it’s an exciting technology with a ton of potential. Find helpful customer reviews and review ratings for Reinforcement Learning: An Introduction at Amazon.com. Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). Ray is an open-source … Introduction to Logic Programming (Synthesis Lectures on Artificial Intelligence an... Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. FREE delivery: 5 - 10 Nov. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. To get the free app, enter your mobile phone number. This is a very readable and comprehensive account of the background, algorithms, applications, and future directions of this pioneering and far-reaching work. Prime members enjoy FREE Delivery and exclusive access to music, movies, TV shows, original audio series, and Kindle books. En el libro faltan los ultimos capitulos, como 10 debido a que se trata de la primera edición y no se reseña en ningun sitio. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. This shopping feature will continue to load items when the Enter key is pressed. Buy Reinforcement Learning With Python: An Introduction (Adaptive Computation and Machine Learning series) 1 by Broad, Stuart (ISBN: 9781974364022) from Amazon's Book Store. Reinforcement Learning: An Introduction Richard S. Sutton and Andrew G. Barto First Edition (see here for second edition) MIT Press, Cambridge, MA, 1998 A Bradford Book. Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) by Richard S. Sutton , Andrew G. Barto , … If anything, the symbolic crafting in this book (and the changes detailing how the symbolic representations have changed) is priceless. Find all the books, read about the author, and more. Find helpful customer reviews and review ratings for Reinforcement Learning – An Introduction (Adaptive Computation and Machine Learning series) at Amazon.com. The representations take a little bit to digest. AWS DeepRacer, AWS DeepRacer Evo, and Sensor Kit are now available for shipping outside the United States for purchase on Amazon.com.AWS DeepRacer is the fastest way to get rolling with reinforcement learning (RL), with a fully autonomous 1/18th scale race car driven by reinforcement learning. Looks like fake especially when compared with my friend's (the one on the left in pictures), Reviewed in the United States on April 6, 2019. There was an error retrieving your Wish Lists. They answered all the questions I have and try to solve all my concerns. But the first person to cross the finish line shall be immortalized like a god.

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