Download or read book Autonomous Inter task Transfer in Reinforcement Learning Domains written by Matthew Edmund Taylor and published by . This book was released on 2008 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) methods have become popular in recent years because of their ability to solve complex tasks with minimal feedback. While these methods have had experimental successes and have been shown to exhibit some desirable properties in theory, the basic learning algorithms have often been found slow in practice. Therefore, much of the current RL research focuses on speeding up learning by taking advantage of domain knowledge, or by better utilizing agents' experience. The ambitious goal of transfer learning, when applied to RL tasks, is to accelerate learning on some target task after training on a different, but related, source task. This dissertation demonstrates that transfer learning methods can successfully improve learning in RL tasks via experience from previously learned tasks. Transfer learning can increase RL's applicability to difficult tasks by allowing agents to generalize their experience across learning problems. This dissertation presents inter-task mappings, the first transfer mechanism in this area to successfully enable transfer between tasks with different state variables and actions. Inter-task mappings have subsequently been used by a number of transfer researchers. A set of six transfer learning algorithms are then introduced. While these transfer methods differ in terms of what base RL algorithms they are compatible with, what type of knowledge they transfer, and what their strengths are, all utilize the same inter-task mapping mechanism. These transfer methods can all successfully use mappings constructed by a human from domain knowledge, but there may be situations in which domain knowledge is unavailable, or insufficient, to describe how two given tasks are related. We therefore also study how inter-task mappings can be learned autonomously by leveraging existing machine learning algorithms. Our methods use classification and regression techniques to successfully discover similarities between data gathered in pairs of tasks, culminating in what is currently one of the most robust mapping-learning algorithms for RL transfer. Combining transfer methods with these similarity-learning algorithms allows us to empirically demonstrate the plausibility of autonomous transfer. We fully implement these methods in four domains (each with different salient characteristics), show that transfer can significantly improve an agent's ability to learn in each domain, and explore the limits of transfer's applicability.
Download or read book Transfer in Reinforcement Learning Domains written by Matthew Taylor and published by Springer Science & Business Media. This book was released on 2009-06-05 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow or infeasible when RL agents begin with no prior knowledge. The key insight behind "transfer learning" is that generalization may occur not only within tasks, but also across tasks. While transfer has been studied in the psychological literature for many years, the RL community has only recently begun to investigate the benefits of transferring knowledge. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research. The key contributions of this book are: Definition of the transfer problem in RL domains Background on RL, sufficient to allow a wide audience to understand discussed transfer concepts Taxonomy for transfer methods in RL Survey of existing approaches In-depth presentation of selected transfer methods Discussion of key open questions By way of the research presented in this book, the author has established himself as the pre-eminent worldwide expert on transfer learning in sequential decision making tasks. A particular strength of the research is its very thorough and methodical empirical evaluation, which Matthew presents, motivates, and analyzes clearly in prose throughout the book. Whether this is your initial introduction to the concept of transfer learning, or whether you are a practitioner in the field looking for nuanced details, I trust that you will find this book to be an enjoyable and enlightening read. Peter Stone, Associate Professor of Computer Science
Download or read book Transfer in Reinforcement Learning Domains written by Matthew Taylor and published by Springer. This book was released on 2009-05-19 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow or infeasible when RL agents begin with no prior knowledge. The key insight behind "transfer learning" is that generalization may occur not only within tasks, but also across tasks. While transfer has been studied in the psychological literature for many years, the RL community has only recently begun to investigate the benefits of transferring knowledge. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research. The key contributions of this book are: Definition of the transfer problem in RL domains Background on RL, sufficient to allow a wide audience to understand discussed transfer concepts Taxonomy for transfer methods in RL Survey of existing approaches In-depth presentation of selected transfer methods Discussion of key open questions By way of the research presented in this book, the author has established himself as the pre-eminent worldwide expert on transfer learning in sequential decision making tasks. A particular strength of the research is its very thorough and methodical empirical evaluation, which Matthew presents, motivates, and analyzes clearly in prose throughout the book. Whether this is your initial introduction to the concept of transfer learning, or whether you are a practitioner in the field looking for nuanced details, I trust that you will find this book to be an enjoyable and enlightening read. Peter Stone, Associate Professor of Computer Science
Download or read book Transfer Learning for Multiagent Reinforcement Learning Systems written by Felipe Felipe Leno da Silva and published by Springer Nature. This book was released on 2022-06-01 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment. However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning. This book surveys the literature on knowledge reuse in multiagent RL. The authors define a unifying taxonomy of state-of-the-art solutions for reusing knowledge, providing a comprehensive discussion of recent progress in the area. In this book, readers will find a comprehensive discussion of the many ways in which knowledge can be reused in multiagent sequential decision-making tasks, as well as in which scenarios each of the approaches is more efficient. The authors also provide their view of the current low-hanging fruit developments of the area, as well as the still-open big questions that could result in breakthrough developments. Finally, the book provides resources to researchers who intend to join this area or leverage those techniques, including a list of conferences, journals, and implementation tools. This book will be useful for a wide audience; and will hopefully promote new dialogues across communities and novel developments in the area.
Download or read book Reinforcement Learning written by Marco Wiering and published by Springer Science & Business Media. This book was released on 2012-03-05 with total page 653 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research. Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge representation in reinforcement learning settings.
Download or read book Scalable Uncertainty Management written by Steven Schockaert and published by Springer. This book was released on 2016-08-29 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Conference on Scalable Uncertainty Management, SUM 2016, held in Nice, France, in September 2016. The 18 regular papers and 5 short papers were carefully reviewed and selected from 35 submissions. Papers are solicited in all areas of managing and reasoning with substantial and complex kinds of uncertain, incomplete or inconsistent information. These include (but are not restricted to) applications in decision support systems, risk analysis, machine learning, belief networks, logics of uncertainty, belief revision and update, argumentation, negotiation technologies, semantic web applications, search engines, ontology systems, information fusion, information retrieval, natural language processing, information extraction, image recognition, vision systems, data and text mining, and the consideration of issues such as provenance, trust, heterogeneity, and complexity of data and knowledge.
Download or read book Machine Learning and Knowledge Discovery in Databases written by Walter Daelemans and published by Springer Science & Business Media. This book was released on 2008-09-04 with total page 714 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
Download or read book Recent Advances in Reinforcement Learning written by Scott Sanner and published by Springer. This book was released on 2012-05-19 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised and selected papers of the 9th European Workshop on Reinforcement Learning, EWRL 2011, which took place in Athens, Greece in September 2011. The papers presented were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections online reinforcement learning, learning and exploring MDPs, function approximation methods for reinforcement learning, macro-actions in reinforcement learning, policy search and bounds, multi-task and transfer reinforcement learning, multi-agent reinforcement learning, apprenticeship and inverse reinforcement learning and real-world reinforcement learning.
Download or read book Transfer Learning written by Qiang Yang and published by Cambridge University Press. This book was released on 2020-02-13 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: This in-depth tutorial for students, researchers, and developers covers foundations, plus applications ranging from search to multimedia.
Download or read book Artificial General Intelligence 2008 written by Pei Wang and published by IOS Press. This book was released on 2008 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: Includes full-length papers, short position statements and also the papers presented in the post conference workshop on the sociocultural, ethical and futurological implications of Artificial General Intelligence (AGI).
Download or read book Hybrid Metaheuristics written by El-ghazali Talbi and published by Springer. This book was released on 2012-07-31 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main goal of this book is to provide a state of the art of hybrid metaheuristics. The book provides a complete background that enables readers to design and implement hybrid metaheuristics to solve complex optimization problems (continuous/discrete, mono-objective/multi-objective, optimization under uncertainty) in a diverse range of application domains. Readers learn to solve large scale problems quickly and efficiently combining metaheuristics with complementary metaheuristics, mathematical programming, constraint programming and machine learning. Numerous real-world examples of problems and solutions demonstrate how hybrid metaheuristics are applied in such fields as networks, logistics and transportation, bio-medical, engineering design, scheduling.
Download or read book ECAI 2016 written by G.A. Kaminka and published by IOS Press. This book was released on 2016-08-24 with total page 1860 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence continues to be one of the most exciting and fast-developing fields of computer science. This book presents the 177 long papers and 123 short papers accepted for ECAI 2016, the latest edition of the biennial European Conference on Artificial Intelligence, Europe’s premier venue for presenting scientific results in AI. The conference was held in The Hague, the Netherlands, from August 29 to September 2, 2016. ECAI 2016 also incorporated the conference on Prestigious Applications of Intelligent Systems (PAIS) 2016, and the Starting AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume; the papers from STAIRS are published in a separate volume in the Frontiers in Artificial Intelligence and Applications (FAIA) series. Organized by the European Association for Artificial Intelligence (EurAI) and the Benelux Association for Artificial Intelligence (BNVKI), the ECAI conference provides an opportunity for researchers to present and hear about the very best research in contemporary AI. This proceedings will be of interest to all those seeking an overview of the very latest innovations and developments in this field.
Download or read book Autonomous Agents written by Vedran Kordic and published by BoD – Books on Demand. This book was released on 2010-06-01 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi agent systems involve a team of agents working together socially to accomplish a task. An agent can be social in many ways. One is when an agent helps others in solving complex problems. The field of multi agent systems investigates the process underlying distributed problem solving and designs some protocols and mechanisms involved in this process. This book presents a combination of different research issues which are pursued by researchers in the domain of multi agent systems.
Download or read book Computer Games written by Tristan Cazenave and published by Springer. This book was released on 2016-05-11 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Fourth Computer Games Workshop, CGW 2015, and the Fourth Workshop on General Intelligence in Game-Playing Agents, GIGA 2015, held in conjunction with the 24th International Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, in July 2015.The 12 revised full papers presented were carefully reviewed and selected from 27 submissions. The papers address all aspects of artificial intelligence and computer game playing. They discuss topics such as Monte-Carlo methods; heuristic search; board games; card games; video games; perfect and imperfect information games; puzzles and single player games; multi-player games; combinatorial game theory; applications; computational creativity; computational game theory; evaluation and analysis; game design; knowledge representation; machine learning; multi-agent systems; opponent modeling; planning; reasoning; search.
Download or read book Value Based Planning for Teams of Agents in Stochastic Partially Observable Environments written by Frans Oliehoek and published by Amsterdam University Press. This book was released on 2010 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis decision-making problems are formalized using a stochastic discrete-time model called decentralized partially observable Markov decision process (Dec-POMDP).
Download or read book Optinformatics in Evolutionary Learning and Optimization written by Liang Feng and published by Springer Nature. This book was released on 2021-03-29 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers the recent algorithmic advances towards realizing the notion of optinformatics in evolutionary learning and optimization. The book also provides readers a variety of practical applications, including inter-domain learning in vehicle route planning, data-driven techniques for feature engineering in automated machine learning, as well as evolutionary transfer reinforcement learning. Through reading this book, the readers will understand the concept of optinformatics, recent research progresses in this direction, as well as particular algorithm designs and application of optinformatics. Evolutionary algorithms (EAs) are adaptive search approaches that take inspiration from the principles of natural selection and genetics. Due to their efficacy of global search and ease of usage, EAs have been widely deployed to address complex optimization problems occurring in a plethora of real-world domains, including image processing, automation of machine learning, neural architecture search, urban logistics planning, etc. Despite the success enjoyed by EAs, it is worth noting that most existing EA optimizers conduct the evolutionary search process from scratch, ignoring the data that may have been accumulated from different problems solved in the past. However, today, it is well established that real-world problems seldom exist in isolation, such that harnessing the available data from related problems could yield useful information for more efficient problem-solving. Therefore, in recent years, there is an increasing research trend in conducting knowledge learning and data processing along the course of an optimization process, with the goal of achieving accelerated search in conjunction with better solution quality. To this end, the term optinformatics has been coined in the literature as the incorporation of information processing and data mining (i.e., informatics) techniques into the optimization process. The primary market of this book is researchers from both academia and industry, who are working on computational intelligence methods and their applications. This book is also written to be used as a textbook for a postgraduate course in computational intelligence emphasizing methodologies at the intersection of optimization and machine learning.
