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Author : Mohammad A. Al-Ansari
Publisher :
ISBN 13 :
Total Pages : 160 pages
Book Rating : 4.:/5 (59 download)
Download or read book Efficient Reinforcement Learning in Continuous Environments written by Mohammad A. Al-Ansari and published by . This book was released on 2001 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Milad Farsi
Publisher : John Wiley & Sons
ISBN 13 : 111980857X
Total Pages : 276 pages
Book Rating : 4.1/5 (198 download)
Download or read book Model-Based Reinforcement Learning written by Milad Farsi and published by John Wiley & Sons. This book was released on 2023-01-05 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory—optimal control and dynamic programming – or on algorithms—most of which are simulation-based. Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework—from design to application—of a more tractable model-based reinforcement learning technique. Model-Based Reinforcement Learning readers will also find: A useful textbook to use in graduate courses on data-driven and learning-based control that emphasizes modeling and control of dynamical systems from data Detailed comparisons of the impact of different techniques, such as basic linear quadratic controller, learning-based model predictive control, model-free reinforcement learning, and structured online learning Applications and case studies on ground vehicles with nonholonomic dynamics and another on quadrator helicopters An online, Python-based toolbox that accompanies the contents covered in the book, as well as the necessary code and data Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.
Author : Marc Peter Deisenroth
Publisher : KIT Scientific Publishing
ISBN 13 : 3866445695
Total Pages : 226 pages
Book Rating : 4.8/5 (664 download)
Download or read book Efficient Reinforcement Learning Using Gaussian Processes written by Marc Peter Deisenroth and published by KIT Scientific Publishing. This book was released on 2010 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.
Author : Fei Feng
Publisher :
ISBN 13 :
Total Pages : 177 pages
Book Rating : 4.:/5 (124 download)
Download or read book Efficient Reinforcement Learning in Various Environments: from the Idealized to the Realistic written by Fei Feng and published by . This book was released on 2021 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: How to achieve efficient reinforcement learning in various training environments is a central challenge in artificial intelligence. This thesis investigates this question on the spectrum of environments from the most idealized type to a fairly realistic one. We use two characteristics to describe the complexity of an environment: 1. how many observations it contains; 2. how difficult it is to capture high rewards. Based on these two scales, we study four types of environments: 1. finite (a small number of) observations plus a generative model (one of the most idealized sample oracles); 2. finite observations plus an approximate model; 3. rich (possibly infinitely many) but structured observations with an online simulation model; 4. general rich observations with an online simulation model. From the first to the last, the problem becomes more and more difficult and significant to solve. This thesis provides novel algorithms/analyses for each setting to improve both statistical and computational efficiency upon prior work.
Author : Ali Nouri
Publisher :
ISBN 13 :
Total Pages : 169 pages
Book Rating : 4.:/5 (76 download)
Download or read book Efficient Model-based Exploration in Continuous State-space Environments written by Ali Nouri and published by . This book was released on 2011 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: The impetus for exploration in reinforcement learning (RL) is decreasing uncertainty about the environment for the purpose of better decision making. As such, exploration plays a crucial role in the efficiency of RL algorithms. In this dissertation, I consider continuous state control problems and introduce a new methodology for representing uncertainty that engenders more efficient algorithms. I argue that the new notion of uncertainty allows for more efficient use of function approximation, which is essential for learning in continuous spaces. In particular, I focus on a class of algorithms referred to as model-based methods and develop several such algorithms that are much more efficient than the current state-of-the-art methods. These algorithms attack the long-standing "curse of dimensionality''-- learning complexity often scales exponentially with problem dimensionality. I introduce algorithms that can exploit the dependency structure between state variables to exponentially decrease the sample complexity of learning, both in cases where the dependency structure is provided by the user a priori and cases where the algorithm has to find it on its own. I also use the new uncertainty notion to derive a multi-resolution exploration scheme, and demonstrate how this new technique achieves anytime behavior, which is very important in real-life applications. Finally, using a set of rich experiments, I show how the new exploration mechanisms affect the efficiency of learning, especially in real-life domains where acquiring samples is expensive.
Author : Shi Dong (Researcher of reinforcement learning)
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (135 download)
Download or read book Efficient Reinforcement Learning with Agent States written by Shi Dong (Researcher of reinforcement learning) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a wide range of decision problems, much focus of academic research has been put on stylized models, whose capacities are usually limited by problem-specific assumptions. In the previous decade, approaches based on reinforcement learning (RL) have received growing attention. With these approaches, a unified method can be applied to a broad class of problems, circumventing the need for stylized solutions. Moreover, when it comes to real-life applications, such RL-based approaches, unfettered from the constraining models, can potentially leverage the growing amount of data and computational resources. As such, continuing innovations might empower RL to tackle problems in the complex physical world. So far, empirical accomplishments of RL have largely been limited to artificial environments, such as games. One reason is that the success of RL often hinges on the availability of a simulator that is able to mass-produce samples. Meanwhile, real environments, such as medical facilities, fulfillment centers, and the World Wide Web, exhibit complex dynamics that can hardly be captured by hard-coded simulators. To bring the achievement of RL into practice, it would be useful to think in terms of how the interactions between the agent and the real world ought to be modeled. Recent works on RL theory tend to focus on restrictive classes of environments that fail to capture certain aspects of the real world. For example, many of such works model the environment as a Markov Decision Process (MDP), which requires that the agent always observe a summary statistic of its situation. In practice, this means that the agent designer has to identify a set of "environmental states, " where each state incorporates all information about the environment relevant to decision-making. Moreover, to ensure that the agent learns from its trajectories, MDP models presume that some environmental states are visited infinitely often. This could be a significant simplification of the real world, as the gifted Argentine poet Jorge Luis Borges once said, "Every day, perhaps every hour, is different." To generate insights on agent design in authentic applications, in this dissertation we consider a more general framework of RL that relaxes such restrictions. Specifically, we demonstrate a simple RL agent that implements an optimistic version of Q-learning and establish through regret analysis that this agent can operate with some level of competence in any environment. While we leverage concepts from the literature on provably efficient RL, we consider a general agent-environment interface and provide a novel agent design and analysis that further develop the concept of agent state, which is defined as the collection of information that the agent maintains in order to make decisions. This level of generality positions our results to inform the design of future agents for operation in complex real environments. We establish that, as time progresses, our agent performs competitively relative to policies that require longer times to evaluate. The time it takes to approach asymptotic performance is polynomial in the complexity of the agent's state representation and the time required to evaluate the best policy that the agent can represent. Notably, there is no dependence on the complexity of the environment. The ultimate per-period performance loss of the agent is bounded by a constant multiple of a measure of distortion introduced by the agent's state representation. Our work is the first to establish that an algorithm approaches this asymptotic condition within a tractable time frame, and the results presented in this dissertation resolve multiple open issues in approximate dynamic programming.
Author : Todd Hester
Publisher : Springer
ISBN 13 : 3319011685
Total Pages : 170 pages
Book Rating : 4.3/5 (19 download)
Download or read book TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains written by Todd Hester and published by Springer. This book was released on 2013-06-22 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuous state features; 3) it must handle sensor and/or actuator delays; and 4) it should continually select actions in real time. This book focuses on addressing all four of these challenges. In particular, this book is focused on time-constrained domains where the first challenge is critically important. In these domains, the agent’s lifetime is not long enough for it to explore the domains thoroughly, and it must learn in very few samples.
Author : Lutz Frommberger
Publisher : Springer Science & Business Media
ISBN 13 : 3642165907
Total Pages : 186 pages
Book Rating : 4.6/5 (421 download)
Download or read book Qualitative Spatial Abstraction in Reinforcement Learning written by Lutz Frommberger and published by Springer Science & Business Media. This book was released on 2010-12-13 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems. Furthermore, acquired knowledge specific to the learned task, and transfer of knowledge to new tasks is crucial. In this book the author investigates whether deficiencies of reinforcement learning can be overcome by suitable abstraction methods. He discusses various forms of spatial abstraction, in particular qualitative abstraction, a form of representing knowledge that has been thoroughly investigated and successfully applied in spatial cognition research. With his approach, he exploits spatial structures and structural similarity to support the learning process by abstracting from less important features and stressing the essential ones. The author demonstrates his learning approach and the transferability of knowledge by having his system learn in a virtual robot simulation system and consequently transfer the acquired knowledge to a physical robot. The approach is influenced by findings from cognitive science. The book is suitable for researchers working in artificial intelligence, in particular knowledge representation, learning, spatial cognition, and robotics.
Author : Kaushik Balakrishnan
Publisher : Packt Publishing Ltd
ISBN 13 : 1789533449
Total Pages : 175 pages
Book Rating : 4.7/5 (895 download)
Download or read book TensorFlow Reinforcement Learning Quick Start Guide written by Kaushik Balakrishnan and published by Packt Publishing Ltd. This book was released on 2019-03-30 with total page 175 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.Formulate and devise selective algorithms and techniques in your applications in no time.Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems. What you will learnUnderstand the theory and concepts behind modern Reinforcement Learning algorithmsCode state-of-the-art Reinforcement Learning algorithms with discrete or continuous actionsDevelop Reinforcement Learning algorithms and apply them to training agents to play computer gamesExplore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlowUse A3C to play CartPole and LunarLanderTrain an agent to drive a car autonomously in a simulatorWho this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.
Author : Claude-Nicolas Fiechter
Publisher :
ISBN 13 :
Total Pages : 125 pages
Book Rating : 4.:/5 (854 download)
Download or read book Design and Analysis of Efficient Reinforcement Learning Algorithms written by Claude-Nicolas Fiechter and published by . This book was released on 1997 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning considers the problem of learning a task or behavior by interacting with one's environment. The learning agent is not explicitly told how the task is to be achieved and has to learn by trial-and-error, using only the rewards and punishments that it receives in response to the actions it takes. In the last ten years there has been a rapidly growing interest in reinforcement learning techniques as a base for intelligent control architectures. Many methods have been proposed and a number of very successful applications have been developed. This dissertation contributes to a theoretical foundation for the study of reinforcement learning by applying some of the methods and tools of computational learning theory to the problem. We propose a formal model of efficient reinforcement learning based on Valiant's Probably Approximately Correct (PAC) learning framework, and use it to design reinforcement learning algorithms and to analyze their performance. We describe the first polynomial-time PAC algorithm for the general finite-state reinforcement learning problem and show that an active and directed exploration of its environment by the learning agent is necessary and sufficient to obtain efficient learning for that problem. We consider the trade-off between exploration and exploitation in reinforcement learning algorithms and show how in general an off-line PAC algorithm can be converted into an on-line algorithm that efficiently balances exploration and exploitation. We also consider the problem of generalization in reinforcement learning and show how in some cases the underlying structure of the environment can be exploited to achieve faster learning. We describe a PAC algorithm for the associative reinforcement learning problem that uses a form of decision lists to represent the policies in a compact way and generalize across different inputs. In addition, we describe a PAC algorithm for a special case of reinforcement learning where the environment can be modeled by a linear system. This particular reinforcement learning problem corresponds to the so-called linear quadratic regulator which is extensively studied and used in automatic and adaptive control.
Author : Miguel Morales
Publisher : Simon and Schuster
ISBN 13 : 1638356661
Total Pages : 470 pages
Book Rating : 4.6/5 (383 download)
Download or read book Grokking Deep Reinforcement Learning written by Miguel Morales and published by Simon and Schuster. This book was released on 2020-10-15 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback. Summary We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology We learn by interacting with our environment, and the rewards or punishments we experience guide our future behavior. Deep reinforcement learning brings that same natural process to artificial intelligence, analyzing results to uncover the most efficient ways forward. DRL agents can improve marketing campaigns, predict stock performance, and beat grand masters in Go and chess. About the book Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback. What's inside An introduction to reinforcement learning DRL agents with human-like behaviors Applying DRL to complex situations About the reader For developers with basic deep learning experience. About the author Miguel Morales works on reinforcement learning at Lockheed Martin and is an instructor for the Georgia Institute of Technology’s Reinforcement Learning and Decision Making course. Table of Contents 1 Introduction to deep reinforcement learning 2 Mathematical foundations of reinforcement learning 3 Balancing immediate and long-term goals 4 Balancing the gathering and use of information 5 Evaluating agents’ behaviors 6 Improving agents’ behaviors 7 Achieving goals more effectively and efficiently 8 Introduction to value-based deep reinforcement learning 9 More stable value-based methods 10 Sample-efficient value-based methods 11 Policy-gradient and actor-critic methods 12 Advanced actor-critic methods 13 Toward artificial general intelligence
Author : Shixiang Gu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (112 download)
Download or read book Sample-efficient Deep Reinforcement Learning for Continuous Control written by Shixiang Gu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Richard S. Sutton
Publisher : MIT Press
ISBN 13 : 0262352702
Total Pages : 549 pages
Book Rating : 4.2/5 (623 download)
Download or read book Reinforcement Learning, second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. 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. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. 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. 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. The final chapter discusses the future societal impacts of reinforcement learning.
Author : Dongruo Zhou
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)
Download or read book Efficient Reinforcement Learning Through Uncertainties written by Dongruo Zhou and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is centered around the concept of uncertainty-aware reinforcement learning (RL), which seeks to enhance the efficiency of RL by incorporating uncertainty. RL is a vital mathematical framework in the field of artificial intelligence (AI) for creating autonomous agents that can learn optimal behaviors through interaction with their environments. However, RL is often criticized for being sample inefficient and computationally demanding. To tackle these challenges, the primary goals of this dissertation are twofold: to offer theoretical understanding of uncertainty-aware RL and to develop practical algorithms that utilize uncertainty to enhance the efficiency of RL. Our first objective is to develop an RL approach that is efficient in terms of sample usage for Markov Decision Processes (MDPs) with large state and action spaces. We present an uncertainty-aware RL algorithm that incorporates function approximation. We provide theoretical proof that this algorithm achieves near minimax optimal statistical complexity when learning the optimal policy. In our second objective, we address two specific scenarios: the batch learning setting and the rare policy switch setting. For both settings, we propose uncertainty-aware RL algorithms with limited adaptivity. These algorithms significantly reduce the number of policy switches compared to previous baseline algorithms while maintaining a similar level of statistical complexity. Lastly, we focus on estimating uncertainties in neural network-based estimation models. We introduce a gradient-based method that effectively computes these uncertainties. Our approach is computationally efficient, and the resulting uncertainty estimates are both valid and reliable. The methods and techniques presented in this dissertation contribute to the advancement of our understanding regarding the fundamental limits of RL. These research findings pave the way for further exploration and development in the field of decision-making algorithm design.
Author : Max Schwarzer
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (125 download)
Download or read book Data-efficient Reinforcement Learning with Self-predictive Representations written by Max Schwarzer and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Data efficiency remains a key challenge in deep reinforcement learning. Although modern techniques have been shown to be capable of attaining high performance in extremely complex tasks, including strategy games such as StarCraft, Chess, Shogi, and Go as well as in challenging visual domains such as Atari games, doing so generally requires enormous amounts of interactional data, limiting how broadly reinforcement learning can be applied. In this thesis, we propose SPR, a method drawing from recent advances in self-supervised representation learning designed to enhance the data efficiency of deep reinforcement learning agents. We evaluate this method on the Atari Learning Environment, and show that it dramatically improves performance with limited computational overhead. When given roughly the same amount of learning time as human testers, a reinforcement learning agent augmented with SPR achieves super-human performance on 7 out of 26 games, an increase of 350% over the previous state of the art, while also strongly improving mean and median performance. We also evaluate this method on a set of continuous control tasks, showing substantial improvements over previous methods. Chapter 1 introduces concepts necessary to understand the work presented, including overviews of Deep Reinforcement Learning and Self-Supervised Representation learning. Chapter 2 contains a detailed description of our contributions towards leveraging self-supervised representation learning to improve data-efficiency in reinforcement learning. Chapter 3 provides some conclusions drawn from this work, including a number of proposals for future work.
Author : Carlos Gregorio Diuk Wasser
Publisher :
ISBN 13 :
Total Pages : 133 pages
Book Rating : 4.:/5 (696 download)
Download or read book An Object-oriented Representation for Efficient Reinforcement Learning written by Carlos Gregorio Diuk Wasser and published by . This book was released on 2010 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: Agents (humans, mice, computers) need to constantly make decisions to survive and thrive in their environment. In the reinforcement-learning problem, an agent needs to learn to maximize its long-term expected reward through direct interaction with the world. To achieve this goal, the agent needs to build some sort of internal representation of the relationship between its actions, the state of the world and the reward it expects to obtain. In this work, I show how the way in which the agent represents state and models the world plays a key role in its ability to learn effectively. I will introduce a new representation, based on objects and their interactions, and show how it enables several orders of magnitude faster learning on a large class of problems. I claim that this representation is a natural way of modeling state and that it bridges a gap between generality and tractability in a broad and interesting class of domains, namely those of relational nature. I will present a set of learning algorithms that make use of this representation in both deterministic and stochastic environments, and present polynomial bounds that prove their efficiency in terms of learning complexity.
Author : Luigi Piroddi
Publisher : Springer Nature
ISBN 13 : 3030859185
Total Pages : 151 pages
Book Rating : 4.0/5 (38 download)
Download or read book Special Topics in Information Technology written by Luigi Piroddi and published by Springer Nature. This book was released on 2022-01-01 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists.
