Download or read book Policy gradient Algorithms for Partially Observable Markov Decision Processes written by Douglas Alexander Aberdeen and published by . This book was released on 2003 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Using Exact Models to Analyze Policy Gradient Algorithms written by Gavin McCracken and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Policy gradient methods are extensively used in reinforcement learning as a way to optimize expected return. In this thesis, we build upon the idea that in general, the evolution of a policy's parameters, when updated by various forms of policy gradient, can be modelled as a continuous state Markov chain. This Markov chain has transition probabilities determined by the gradient of the distribution of the policy's value and for some suitable environments can be analyzed directly, providing insight into the properties of the policy gradient algorithm, without the need for empirical analysis through randomized runs. We introduce a novel class of exactly solvable Partially Observable Markov Decision Processes (POMDPs). Constructing these POMDPs relies heavily on random walk theory, specifically on affine Weyl groups. The resulting models allow the value distribution, and hence the policy parameter evolution, to be derived analytically. To our knowledge, this is the first known class of problems allowing to analytically compute the landscape of policy gradient for a POMDP class, allowing us to inspect how the parameter initialization, algorithm hyperparameter choices and gradient optimizer change the distributional convergence to different maxima in the value function. This class of problems can be viewed as a useful, detailed analysis tool"--
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 Markov Decision Processes in Artificial Intelligence written by Olivier Sigaud and published by John Wiley & Sons. This book was released on 2013-03-04 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as reinforcement learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in artificial intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, reinforcement learning, partially observable MDPs, Markov games and the use of non-classical criteria). It then presents more advanced research trends in the field and gives some concrete examples using illustrative real life applications.
Download or read book Approximate Solution Methods for Partially Observable Markov and Semi Markov Decision Processes written by Huizhen Yu (Ph. D.) and published by . This book was released on 2006 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: (Cont.) We thus provide an alternative to the earlier actor-only algorithm GPOMDP. Our work also clarifies the relationship between the reinforcement learning methods for POMDPs and those for MDPs. For average cost MDPs, we provide a convergence and convergence rate analysis for a least squares temporal difference (TD) algorithm, called LSPE, and previously proposed for discounted problems. We use this algorithm in the critic portion of the policy gradient algorithm for POMDPs with finite-state controllers. Finally, we investigate the properties of the limsup and liminf average cost functions of various types of policies. We show various convexity and concavity properties of these costfunctions, and we give a new necessary condition for the optimal liminf average cost to be constant. Based on this condition, we prove the near-optimality of the class of finite-state controllers under the assumption of a constant optimal liminf average cost. This result provides a theoretical guarantee for the finite-state controller approach.
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 Continuous Homomorphisms and Leveraging Symmetries in Policy Gradient Algorithms for Markov Decision Processes written by Rosie Zhao and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Making meaningful abstractions is key to a reinforcement learning agent's ability to make sense of unstructured streams of data, explore intractable spaces, and generalize to new tasks. Symmetries within the environment can be used to make such abstractions; in this thesis, we focus on state abstractions and establish the theoretical foundations of Markov decision processes (MDPs) containing underlying symmetries in the state-action space. In particular, we focus on one definition of behavioural equivalence between states, and present three existing views of capturing such an equivalence: bisimulation relations, symmetry groups, and MDP homomorphisms. We show equivalence between the three views for countable state spaces. We also turn our attention to continuous MDP homomorphisms, establishing theoretical results for these maps on general continuous state and action spaces which previously have not been properly derived. These results include the value-equivalence property, which states that the value functions given a state-action pair in the original MDP are equal to that of their transformed state-action pair in the abstract MDP. Thus, we can introduce new reinforcement learning algorithms that use existing methods on the abstract MDP and ``lift'' the results to the original MDP. In this vein, we prove a result called the stochastic homomorphic policy gradient (HPG) theorem, which shows that the policy gradient on the original MDP can be calculated using the policy gradient on the abstract MDP under an appropriate lifting procedure. We demonstrate the stochastic HPG theorem's utility by proposing a deep reinforcement learning algorithm leveraging the MDP homomorphism on continuous control tasks"--
Download or read book Software Engineering written by Krzysztof Zieliński and published by IOS Press. This book was released on 2005 with total page 1316 pages. Available in PDF, EPUB and Kindle. Book excerpt: The capability to design quality software and implement modern information systems is at the core of economic growth in the 21st century. This book aims to review and analyze software engineering technologies, focusing on the evolution of design and implementation platforms as well as on novel computer systems.
Download or read book Algorithms for Partially Observable Markov Decision Processes written by Hsien-Te Cheng and published by . This book was released on 1988 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Information Processing written by Masumi Ishikawa and published by Springer Science & Business Media. This book was released on 2008-06-16 with total page 1120 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 4984 and LNCS 4985 constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Neural Information Processing, ICONIP 2007, held in Kitakyushu, Japan, in November 2007, jointly with BRAINIT 2007, the 4th International Conference on Brain-Inspired Information Technology. The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models, supervised/unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches.
Download or read book Learning in the Presence of Partial Observability and Concept Drifts written by Raihan Seraj and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis is divided in two parts where we analyse machine learning algorithms in two different contexts. In the first part we study reinforcement learning algorithms, specially policy gradient methods for partially observable Markov decision processes (POMDPs). We use a special class of policy structure represented by a finite state controller. A comparison of different policy based methods are made and the performance of these methods are compared with the existing planning solutions for different POMDP environments used in the literature. The second part of this thesis outlines the problem of concept drifts for time series data where we analyze the temporal inconsistency of streaming wireless signals in the context of device-free passive indoor localization. We highlight that concept drifts play a major role in deteriorating the predictive accuracy of models trained for room level localization with WiFi signals and propose a phase and magnitude augmented feature space that is resistant to drifts. We study different learning algorithms with this new feature space and compare their performance in the presence of drifts." --
Download or read book Partially Observed Markov Decision Processes written by Vikram Krishnamurthy and published by Cambridge University Press. This book was released on 2016-03-21 with total page 491 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers formulation, algorithms, and structural results of partially observed Markov decision processes, whilst linking theory to real-world applications in controlled sensing. Computations are kept to a minimum, enabling students and researchers in engineering, operations research, and economics to understand the methods and determine the structure of their optimal solution.
Download or read book Exploiting Structure to Efficiently Solve Large Scale Partially Observable Markov Decision Processes microform written by Pascal Poupart and published by Library and Archives Canada = Bibliothèque et Archives Canada. This book was released on 2005 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: Partially observable Markov decision processes (POMDPs) provide a natural and principled framework to model a wide range of sequential decision making problems under uncertainty. To date, the use of POMDPs in real-world problems has been limited by the poor scalability of existing solution algorithms, which can only solve problems with up to ten thousand states. In fact, the complexity of finding an optimal policy for a finite-horizon discrete POMDP is PSPACE-complete. In practice, two important sources of intractability plague most solution algorithms: Large policy spaces and large state spaces. In practice, it is critical to simultaneously mitigate the impact of complex policy representations and large state spaces. Hence, this thesis describes three approaches that combine techniques capable of dealing with each source of intractability: VDC with BPI, VDC with Perseus (a randomized point-based value iteration algorithm by Spaan and Vlassis [136]), and state abstraction with Perseus. The scalability of those approaches is demonstrated on two problems with more than 33 million states: synthetic network management and a real-world system designed to assist elderly persons with cognitive deficiencies to carry out simple daily tasks such as hand-washing. This represents an important step towards the deployment of POMDP techniques in ever larger, real-world, sequential decision making problems. On the other hand, for many real-world POMDPs it is possible to define effective policies with simple rules of thumb. This suggests that we may be able to find small policies that are near optimal. This thesis first presents a Bounded Policy Iteration (BPI) algorithm to robustly find a good policy represented by a small finite state controller. Real-world POMDPs also tend to exhibit structural properties that can be exploited to mitigate the effect of large state spaces. To that effect, a value-directed compression (VDC) technique is also presented to reduce POMDP models to lower dimensional representations.
Download or read book Recent Advances in Reinforcement Learning written by Sertan Girgin and published by Springer Science & Business Media. This book was released on 2008-12 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised and selected papers of the 8th European Workshop on Reinforcement Learning, EWRL 2008, which took place in Villeneuve d'Ascq, France, during June 30 - July 3, 2008. The 21 papers presented were carefully reviewed and selected from 61 submissions. They are dedicated to the field of and current researches in reinforcement learning.
Download or read book Intelligent Autonomous Systems 9 written by Tamio Arai and published by IOS Press. This book was released on 2006 with total page 1064 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomy and adaptivity are key aspects of truly intelligent artificial systems, dating from the first IAS conference in 1989. The goal of IAS-9 is to lay out scientific ideas and design principles for artificial systems. This work contains papers that cover both the applied and the theoretical aspects of intelligent autonomous systems.
Download or read book Machine Learning ECML 2004 written by Jean-Francois Boulicaut and published by Springer. This book was released on 2004-11-05 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proceedings of ECML/PKDD 2004 are published in two separate, albeit - tertwined,volumes:theProceedingsofthe 15thEuropeanConferenceonMac- ne Learning (LNAI 3201) and the Proceedings of the 8th European Conferences on Principles and Practice of Knowledge Discovery in Databases (LNAI 3202). The two conferences were co-located in Pisa, Tuscany, Italy during September 20–24, 2004. It was the fourth time in a row that ECML and PKDD were co-located. - ter the successful co-locations in Freiburg (2001), Helsinki (2002), and Cavtat- Dubrovnik (2003), it became clear that researchersstrongly supported the or- nization of a major scienti?c event about machine learning and data mining in Europe. We are happy to provide some statistics about the conferences. 581 di?erent papers were submitted to ECML/PKDD (about a 75% increase over 2003); 280 weresubmittedtoECML2004only,194weresubmittedtoPKDD2004only,and 107weresubmitted to both.Aroundhalfofthe authorsforsubmitted papersare from outside Europe, which is a clear indicator of the increasing attractiveness of ECML/PKDD. The Program Committee members were deeply involved in what turned out to be a highly competitive selection process. We assigned each paper to 3 - viewers, deciding on the appropriate PC for papers submitted to both ECML and PKDD. As a result, ECML PC members reviewed 312 papers and PKDD PC members reviewed 269 papers. We accepted for publication regular papers (45 for ECML 2004 and 39 for PKDD 2004) and short papers that were as- ciated with poster presentations (6 for ECML 2004 and 9 for PKDD 2004). The globalacceptance ratewas14.5%for regular papers(17% if we include the short papers).
Download or read book Artificial Neural Networks ICANN 2008 written by Vera Kurkova-Pohlova and published by Springer. This book was released on 2008-09-08 with total page 1053 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two volume set LNCS 5163 and LNCS 5164 constitutes the refereed proceedings of the 18th International Conference on Artificial Neural Networks, ICANN 2008, held in Prague Czech Republic, in September 2008. The 200 revised full papers presented were carefully reviewed and selected from more than 300 submissions. The first volume contains papers on mathematical theory of neurocomputing, learning algorithms, kernel methods, statistical learning and ensemble techniques, support vector machines, reinforcement learning, evolutionary computing, hybrid systems, self-organization, control and robotics, signal and time series processing and image processing.
