Download or read book Design of Experiments for Reinforcement Learning written by Christopher Gatti and published by Springer. This book was released on 2014-11-22 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.

Download or read book Machine Learning for Experiment Design written by Jashan Jii and published by . This book was released on 2023-10-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Experiment Design: A Review, with a Focus on Active Learning" Experimentation lies at the heart of scientific progress and technological innovation. In recent years, machine learning has emerged as a powerful tool for enhancing the process of experiment design. This comprehensive review delves into the fascinating intersection of machine learning and experiment design, with a particular emphasis on the role of active learning. Experiment design involves making informed decisions about the parameters, variables, and conditions under which experiments are conducted to achieve specific goals. Traditional approaches rely on expert knowledge and trial-and-error methods, often resulting in time-consuming and resource-intensive processes. This is where machine learning steps in, revolutionizing the way experiments are planned and executed. The review begins by providing a solid foundation in the fundamentals of experiment design and its importance across various domains, including chemistry, biology, engineering, and more. It explores how machine learning algorithms, particularly active learning, can assist in the selection of informative data points, reducing the need for large-scale data collection and experimentation. By iteratively choosing the most valuable data points, active learning accelerates the convergence of experimental outcomes, saving time and resources. The discussion also covers the wide array of machine learning techniques employed in experiment design, from Bayesian optimization and reinforcement learning to deep learning approaches. Real-world case studies from diverse fields highlight the effectiveness of these methods in optimizing experimental processes, optimizing resource allocation, and achieving superior results. Furthermore, the review addresses the ethical considerations surrounding the use of machine learning in experiment design, emphasizing the importance of transparency, bias mitigation, and responsible data management. "Machine Learning for Experiment Design: A Review, with a Focus on Active Learning" serves as an invaluable resource for researchers, scientists, and engineers seeking to harness the potential of machine learning to enhance the efficiency, accuracy, and innovation of their experiments. It offers insights into the state of the art in this dynamic field and charts a course for the future of experiment design, where intelligent algorithms work hand in hand with human expertise to unlock new discoveries and advancements.

Download or read book Some Experiments with Reinforcement Learning on Real World Robots written by Shardul Vikram and published by . This book was released on 2001 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Experiments and Analysis in Stabilizing Human Control Strategies Through Reinforcement Learning written by Nihat Kasap and published by . This book was released on 2000 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Methods and Applications of Autonomous Experimentation written by Marcus Noack and published by CRC Press. This book was released on 2023-12-14 with total page 575 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous Experimentation is poised to revolutionize scientific experiments at advanced experimental facilities. Whereas previously, human experimenters were burdened with the laborious task of overseeing each measurement, recent advances in mathematics, machine learning and algorithms have alleviated this burden by enabling automated and intelligent decision-making, minimizing the need for human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experiences, this book is a practical guide to successful Autonomous Experimentation. Despite the field’s growing potential, there exists numerous myths and misconceptions surrounding Autonomous Experimentation. Combining insights from theorists, machine-learning engineers and applied scientists, this book aims to lay the foundation for future research and widespread adoption within the scientific community. This book is particularly useful for members of the scientific community looking to improve their research methods but also contains additional insights for students and industry professionals interested in the future of the field.

Download or read book Reinforcement Learning From Scratch written by Uwe Lorenz and published by Springer. This book was released on 2023-10-28 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In ancient games such as chess or go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work? With easy-to-understand explanations and clear examples in Java and Greenfoot, you can acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.Kölling, King's College London) and the hamster model (D. Bohles, University of Oldenburg) are simple but also powerful didactic tools that were developed to convey basic programming concepts. The result is an accessible introduction into machine learning that concentrates on reinforcement learning. Taking the reader through the steps of developing intelligent agents, from the very basics to advanced aspects, touching on a variety of machine learning algorithms along the way, one is allowed to play along, experiment, and add their own ideas and experiments.

Download or read book Reinforcement Learning Bit by Bit written by Xiuyuan Lu and published by . This book was released on 2023-07-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning agents have demonstrated remarkable achievements in simulated environments. Data efficiency, however, significantly impedes carrying this success over to real environments. The design of data-efficient agents that address this problem calls for a deeper understanding of information acquisition and representation. This tutorial offers a framework that can guide associated agent design decisions. This framework is inspired in part by concepts from information theory that has grappled with data efficiency for many years in the design of communication systems. In this tutorial, the authors shed light on questions of what information to seek, how to seek that information, and what information to retain. To illustrate the concepts, they design simple agents that build on them and present computational results that highlight data efficiency. This book will be of interest to students and researchers working in reinforcement learning and information theorists wishing to apply their knowledge in a practical way to reinforcement learning problems.

Download or read book Design Experiments and Implementation of Reinforcement Learning in RSTAR Robot for Search and Rescue Applications written by Liran Yehezkel and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents the Rising STAR (RSTAR) a newly developed crawling robot capable of reconfiguring its shape and moving the position of its center of mass. RSTAR belongs to the family of the STAR robots with similar sprawling capabilities allowing it to run in a planar configuration, either upright or inverted and change its mechanics from the lateral to the sagittal planes. The RSTAR is also fitted with four bar extension mechanism (FBEM) allowing it to extend the distance between its body and legs.

Download or read book Towards an Adaptive Artificial Creature written by Matthew Goode and published by . This book was released on 2001 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Design of Experiments written by John and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Reinforcement Function Design and Bias for Efficient Learning in Mobile Robots written by and published by . This book was released on 1998 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main paradigm in sub-symbolic learning robot domain is the reinforcement learning method. Various techniques have been developed to deal with the memorization/generalization problem, demonstrating the superior ability of artificial neural network implementations. In this paper, the authors address the issue of designing the reinforcement so as to optimize the exploration part of the learning. They also present and summarize works relative to the use of bias intended to achieve the effective synthesis of the desired behavior. Demonstrative experiments involving a self-organizing map implementation of the Q-learning and real mobile robots (Nomad 200 and Khepera) in a task of obstacle avoidance behavior synthesis are described. 3 figs., 5 tabs.

Download or read book Optimal Experimental Design with R written by Dieter Rasch and published by Chapman & Hall/CRC. This book was released on 2019-09-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experimental design is often overlooked in the literature of applied and mathematical statistics: statistics is taught and understood as merely a collection of methods for analyzing data. Consequently, experimenters seldom think about optimal design, including prerequisites such as the necessary sample size needed for a precise answer for an experimental question. Providing a concise introduction to experimental design theory, Optimal Experimental Design with R: Introduces the philosophy of experimental design Provides an easy process for constructing experimental designs and calculating necessary sample size using R programs Teaches by example using a custom made R program package: OPDOE Consisting of detailed, data-rich examples, this book introduces experimenters to the philosophy of experimentation, experimental design, and data collection. It gives researchers and statisticians guidance in the construction of optimum experimental designs using R programs, including sample size calculations, hypothesis testing, and confidence estimation. A final chapter of in-depth theoretical details is included for interested mathematical statisticians.

Download or read book Reinforcement Learning for Optimal Feedback Control written by Rushikesh Kamalapurkar and published by Springer. This book was released on 2018-12-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement Learning for Optimal Feedback Control develops model-based and data-driven reinforcement learning methods for solving optimal control problems in nonlinear deterministic dynamical systems. In order to achieve learning under uncertainty, data-driven methods for identifying system models in real-time are also developed. The book illustrates the advantages gained from the use of a model and the use of previous experience in the form of recorded data through simulations and experiments. The book’s focus on deterministic systems allows for an in-depth Lyapunov-based analysis of the performance of the methods described during the learning phase and during execution. To yield an approximate optimal controller, the authors focus on theories and methods that fall under the umbrella of actor–critic methods for machine learning. They concentrate on establishing stability during the learning phase and the execution phase, and adaptive model-based and data-driven reinforcement learning, to assist readers in the learning process, which typically relies on instantaneous input-output measurements. This monograph provides academic researchers with backgrounds in diverse disciplines from aerospace engineering to computer science, who are interested in optimal reinforcement learning functional analysis and functional approximation theory, with a good introduction to the use of model-based methods. The thorough treatment of an advanced treatment to control will also interest practitioners working in the chemical-process and power-supply industry.

Download or read book New Frontiers in Adaptive Experimental Design for Multi Objective Optimization written by Syrine Belakaria and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of adaptively selecting a sequence of experiments to achieve a goal (aka adaptive experimental design) arises in many real-world settings. A canonical example is the active learning paradigm where we need to iteratively collect labeled data to build predictors with high accuracy. Motivated by the challenges faced by scientists and engineers, this dissertation studies adaptive experimental design algorithms for the purpose of solving a large-class of multi-objective optimization (MOO) problems. Such MOO problems enable many science and engineering applications including drug design, protein engineering, design of materials, and hardware design. For example, in drug design optimization, we need to find drugs that trade-off effectiveness, safety, and cost by performing expensive experiments to evaluate each candidate drug. Similarly, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area using expensive computational simulations to mimic the real hardware.We have the ability to evaluate any candidate input according to the target objectives by performing a costly experiment, where the cost is measured by the resources consumed by the experiment (physical or computational). Our overall goal is to approximate the optimal Pareto set of solutions by minimizing the total resource cost of conducted experiments. The key challenge is how to select the sequence of experiments under uncertainty. This dissertation develops a suite of novel reasoning algorithms based on the principles of information gain per unit resource cost and uncertainty reduction for adaptive experimental design to solve MOO problems. We appropriately instantiate these principles to derive efficient algorithms for the following MOO problem settings, most of which are studied for the first time: 1) The most basic single-fidelity setting, where experiments are expensive and accurate, and we can conduct a single experiment in each iteration; 2) The batch setting where a batch of experiments can be conducted in parallel to accelerate the search process; 3) The constrained setting where we cannot evaluate constraints to identify feasible inputs without performing experiments; 4) The discrete multi-fidelity setting where experiments can vary in the amount of resources consumed and their evaluation accuracy; and 5) The continuous-fidelity setting, where continuous function approximations result in a huge space of experiments. 6) The budget-aware setting where a limited resource budget constraint is enforced requiring us to take a planning approach. Experiments on synthetic and real-world benchmarks from a diverse set of engineering and industrial domains demonstrate that our algorithms significantly improve resource efficiency over prior methods to produce high-quality Pareto solutions.

Download or read book Reinforcement Learning written by John Harley Hiett and published by . This book was released on 1997 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book Motivated Reinforcement Learning written by Kathryn E. Merrick and published by Springer. This book was released on 2010-10-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motivated learning is an emerging research field in artificial intelligence and cognitive modelling. Computational models of motivation extend reinforcement learning to adaptive, multitask learning in complex, dynamic environments – the goal being to understand how machines can develop new skills and achieve goals that were not predefined by human engineers. In particular, this book describes how motivated reinforcement learning agents can be used in computer games for the design of non-player characters that can adapt their behaviour in response to unexpected changes in their environment. This book covers the design, application and evaluation of computational models of motivation in reinforcement learning. The authors start with overviews of motivation and reinforcement learning, then describe models for motivated reinforcement learning. The performance of these models is demonstrated by applications in simulated game scenarios and a live, open-ended virtual world. Researchers in artificial intelligence, machine learning and artificial life will benefit from this book, as will practitioners working on complex, dynamic systems – in particular multiuser, online games.