State Aggregation Algorithms For Learning Probabilistic Models For Robot Control PDF Download
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Author | : Daniel Nikovski |
Publisher | : |
Total Pages | : 164 |
Release | : 2002 |
Genre | : Markov processes |
ISBN | : |
Download State-aggregation Algorithms for Learning Probabilistic Models for Robot Control Book in PDF, ePub and Kindle
Abstract: "This thesis addresses the problem of learning probabilistic representations of dynamical systems with non-linear dynamics and hidden state in the form of partially observable Markov decision process (POMDP) models, with the explicit purpose of using these models for robot control. In contrast to the usual approach to learning probabilistic models, which is based on iterative adjustment of probabilities so as to improve the likelihood of the observed data, the algorithms proposed in this thesis take a different approach -- they reduce the learning problem to that of state aggregation by clustering in an embedding space of delayed coordinates, and subsequently estimating transition probabilities between aggregated states (clusters). This approach has close ties to the dominant methods for system identification in the field of control engineering, although the characteristics of POMDP models require very different algorithmic solutions. Apart from an extensive investigation of the performance of the proposed algorithms in simulation, they are also applied to two robots built in the course of our experiments. The first one is a differential-drive mobile robot with a minimal number of proximity sensors, which has to perform the well-known robotic task of self-localization along the perimeter of its workspace. In comparison to previous neural-net based approaches to the same problem, our algorithm achieved much higher spatial accuracy of localization. The other task is visual servo-control of an under-actuated arm which has to rotate a flying ball attached to it so as to maintain maximal height of rotation with minimal energy expenditure. Even though this problem is intractable for known control engineering methods due to its strongly non-linear dynamics and partially observable state, a control policy obtained by means of policy iteration on a POMDP model learned by our state-aggregation algorithm performed better than several alternative open-loop and closed-loop controllers."
Author | : João Gama |
Publisher | : Springer Science & Business Media |
Total Pages | : 784 |
Release | : 2005-09-22 |
Genre | : Computers |
ISBN | : 3540292438 |
Download Machine Learning: ECML 2005 Book in PDF, ePub and Kindle
This book constitutes the refereed proceedings of the 16th European Conference on Machine Learning, ECML 2005, jointly held with PKDD 2005 in Porto, Portugal, in October 2005. The 40 revised full papers and 32 revised short papers presented together with abstracts of 6 invited talks were carefully reviewed and selected from 335 papers submitted to ECML and 30 papers submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.
Author | : Marco Wiering |
Publisher | : Springer Science & Business Media |
Total Pages | : 653 |
Release | : 2012-03-05 |
Genre | : Technology & Engineering |
ISBN | : 3642276458 |
Download Reinforcement Learning Book in PDF, ePub and Kindle
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.
Author | : |
Publisher | : |
Total Pages | : 778 |
Release | : 2003 |
Genre | : Dissertations, Academic |
ISBN | : |
Download Dissertation Abstracts International Book in PDF, ePub and Kindle
Author | : |
Publisher | : |
Total Pages | : 776 |
Release | : 2001 |
Genre | : Dissertation abstracts |
ISBN | : |
Download American Doctoral Dissertations Book in PDF, ePub and Kindle
Author | : Todd Hester |
Publisher | : Springer |
Total Pages | : 170 |
Release | : 2013-06-22 |
Genre | : Technology & Engineering |
ISBN | : 3319011685 |
Download TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains Book in PDF, ePub and Kindle
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 | : Csaba Grossi |
Publisher | : Springer Nature |
Total Pages | : 89 |
Release | : 2022-05-31 |
Genre | : Computers |
ISBN | : 3031015517 |
Download Algorithms for Reinforcement Learning Book in PDF, ePub and Kindle
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration
Author | : Tan, Ying |
Publisher | : IGI Global |
Total Pages | : 889 |
Release | : 2015-12-09 |
Genre | : Technology & Engineering |
ISBN | : 1466695730 |
Download Handbook of Research on Design, Control, and Modeling of Swarm Robotics Book in PDF, ePub and Kindle
Studies on robotics applications have grown substantially in recent years, with swarm robotics being a relatively new area of research. Inspired by studies in swarm intelligence and robotics, swarm robotics facilitates interactions between robots as well as their interactions with the environment. The Handbook of Research on Design, Control, and Modeling of Swarm Robotics is a collection of the most important research achievements in swarm robotics thus far, covering the growing areas of design, control, and modeling of swarm robotics. This handbook serves as an essential resource for researchers, engineers, graduates, and senior undergraduates with interests in swarm robotics and its applications.
Author | : Richard S. Sutton |
Publisher | : MIT Press |
Total Pages | : 549 |
Release | : 2018-11-13 |
Genre | : Computers |
ISBN | : 0262352702 |
Download Reinforcement Learning, second edition Book in PDF, ePub and Kindle
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 | : Marc Peter Deisenroth |
Publisher | : Foundations and Trends(r) in R |
Total Pages | : 160 |
Release | : 2013-08 |
Genre | : Technology & Engineering |
ISBN | : 9781601987020 |
Download A Survey on Policy Search for Robotics Book in PDF, ePub and Kindle
A Survey on Policy Search for Robotics provides an overview of successful policy search methods in the context of robot learning, where high-dimensional and continuous state-action space challenge any Reinforcement Learning (RL) algorithm. It distinguishes between model-free and model-based policy search methods.