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Planning With Markov Decision Processes
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Book Synopsis Planning with Markov Decision Processes by : Mausam
Download or read book Planning with Markov Decision Processes written by Mausam and published by Morgan & Claypool Publishers. This book was released on 2012 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a concise introduction to the use of Markov Decision Processes for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms.
Book Synopsis Planning with Markov Decision Processes by : Mausam Natarajan
Download or read book Planning with Markov Decision Processes written by Mausam Natarajan and published by Springer Nature. This book was released on 2022-06-01 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes
Book Synopsis Markov Decision Processes in Artificial Intelligence by : Olivier Sigaud
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.
Book Synopsis Elicitation and Planning in Markov Decision Processes with Unknown Rewards by : Pegah Alizadeh
Download or read book Elicitation and Planning in Markov Decision Processes with Unknown Rewards written by Pegah Alizadeh and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov decision processes (MDPs) are models for solving sequential decision problemswhere a user interacts with the environment and adapts her policy by taking numericalreward signals into account. The solution of an MDP reduces to formulate the userbehavior in the environment with a policy function that specifies which action to choose ineach situation. In many real world decision problems, the users have various preferences,and therefore, the gain of actions on states are different and should be re-decoded foreach user. In this dissertation, we are interested in solving MDPs for users with differentpreferences.We use a model named Vector-valued MDP (VMDP) with vector rewards. We propose apropagation-search algorithm that allows to assign a vector-value function to each policyand identify each user with a preference vector on the existing set of preferences wherethe preference vector satisfies the user priorities. Since the user preference vector is notknown we present several methods for solving VMDPs while approximating the user'spreference vector.We introduce two algorithms that reduce the number of queries needed to find the optimalpolicy of a user: 1) A propagation-search algorithm, where we propagate a setof possible optimal policies for the given MDP without knowing the user's preferences.2) An interactive value iteration algorithm (IVI) on VMDPs, namely Advantage-basedValue Iteration (ABVI) algorithm that uses clustering and regrouping advantages. Wealso demonstrate how ABVI algorithm works properly for two different types of users:confident and uncertain.We finally work on a minimax regret approximation method as a method for findingthe optimal policy w.r.t the limited information about user's preferences. All possibleobjectives in the system are just bounded between two higher and lower bounds while thesystem is not aware of user's preferences among them. We propose an heuristic minimaxregret approximation method for solving MDPs with unknown rewards that is faster andless complex than the existing methods in the literature.
Book Synopsis Reinforcement Learning by : Marco Wiering
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.
Book Synopsis Markov Chains and Decision Processes for Engineers and Managers by : Theodore J. Sheskin
Download or read book Markov Chains and Decision Processes for Engineers and Managers written by Theodore J. Sheskin and published by CRC Press. This book was released on 2016-04-19 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and service systems. However, most books on Markov chains or decision processes are often either highly theoretical, with few examples, or highly prescriptive, with little justification for the steps of the algorithms u
Book Synopsis Planning with Approximate and Learned Models of Markov Decision Processes by : Cosmin Păduraru
Download or read book Planning with Approximate and Learned Models of Markov Decision Processes written by Cosmin Păduraru and published by . This book was released on 2007 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Planning, the process of using a model of the world to compute a policy for selecting actions, is a key component of artificial intelligence. Planning in realistic domains poses many challenges, such as dealing with large problem sizes, non-deterministic effects of actions or a priori unknown dynamics. A planning system that addresses these challenges must represent the model compactly, using function approximation, deal with stochastic action effects, and learn the model from experience. Existing methods for planning with approximate and stochastic models, however, make restrictive assumptions about the world's structure. In this thesis, a sampling-based planning method with general function approximation for the stochastic model will be proposed as a less restrictive alternative. Experiments in a continuous, stochastic domain show that the proposed method can be more data-efficient than a model-free alternative. In addition, preliminary theoretical results suggest that, for linear function approximators, an approximate model that only represents expected values may be sufficient for planning. The soundness of planning with approximate models is supported by the general theoretical results in Chapter 3.
Book Synopsis Operations Research and Health Care by : Margaret L. Brandeau
Download or read book Operations Research and Health Care written by Margaret L. Brandeau and published by Springer Science & Business Media. This book was released on 2006-04-04 with total page 870 pages. Available in PDF, EPUB and Kindle. Book excerpt: In both rich and poor nations, public resources for health care are inadequate to meet demand. Policy makers and health care providers must determine how to provide the most effective health care to citizens using the limited resources that are available. This chapter describes current and future challenges in the delivery of health care, and outlines the role that operations research (OR) models can play in helping to solve those problems. The chapter concludes with an overview of this book – its intended audience, the areas covered, and a description of the subsequent chapters. KEY WORDS Health care delivery, Health care planning HEALTH CARE DELIVERY: PROBLEMS AND CHALLENGES 3 1.1 WORLDWIDE HEALTH: THE PAST 50 YEARS Human health has improved significantly in the last 50 years. In 1950, global life expectancy was 46 years [1]. That figure rose to 61 years by 1980 and to 67 years by 1998 [2]. Much of these gains occurred in low- and middle-income countries, and were due in large part to improved nutrition and sanitation, medical innovations, and improvements in public health infrastructure.
Book Synopsis Handbook of Markov Decision Processes by : Eugene A. Feinberg
Download or read book Handbook of Markov Decision Processes written by Eugene A. Feinberg and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Eugene A. Feinberg Adam Shwartz This volume deals with the theory of Markov Decision Processes (MDPs) and their applications. Each chapter was written by a leading expert in the re spective area. The papers cover major research areas and methodologies, and discuss open questions and future research directions. The papers can be read independently, with the basic notation and concepts ofSection 1.2. Most chap ters should be accessible by graduate or advanced undergraduate students in fields of operations research, electrical engineering, and computer science. 1.1 AN OVERVIEW OF MARKOV DECISION PROCESSES The theory of Markov Decision Processes-also known under several other names including sequential stochastic optimization, discrete-time stochastic control, and stochastic dynamic programming-studiessequential optimization ofdiscrete time stochastic systems. The basic object is a discrete-time stochas tic system whose transition mechanism can be controlled over time. Each control policy defines the stochastic process and values of objective functions associated with this process. The goal is to select a "good" control policy. In real life, decisions that humans and computers make on all levels usually have two types ofimpacts: (i) they cost orsavetime, money, or other resources, or they bring revenues, as well as (ii) they have an impact on the future, by influencing the dynamics. In many situations, decisions with the largest immediate profit may not be good in view offuture events. MDPs model this paradigm and provide results on the structure and existence of good policies and on methods for their calculation.
Book Synopsis The Complexity of Planning with Partially-observable Markov Decision Processes by : Martin Mundhenk
Download or read book The Complexity of Planning with Partially-observable Markov Decision Processes written by Martin Mundhenk and published by . This book was released on 2000 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Examples in Markov Decision Processes by : A. B. Piunovskiy
Download or read book Examples in Markov Decision Processes written by A. B. Piunovskiy and published by World Scientific. This book was released on 2013 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This invaluable book provides approximately eighty examples illustrating the theory of controlled discrete-time Markov processes. Except for applications of the theory to real-life problems like stock exchange, queues, gambling, optimal search etc, the main attention is paid to counter-intuitive, unexpected properties of optimization problems. Such examples illustrate the importance of conditions imposed in the theorems on Markov Decision Processes. Many of the examples are based upon examples published earlier in journal articles or textbooks while several other examples are new. The aim was to collect them together in one reference book which should be considered as a complement to existing monographs on Markov decision processes. The book is self-contained and unified in presentation. The main theoretical statements and constructions are provided, and particular examples can be read independently of others. Examples in Markov Decision Processes is an essential source of reference for mathematicians and all those who apply the optimal control theory to practical purposes. When studying or using mathematical methods, the researcher must understand what can happen if some of the conditions imposed in rigorous theorems are not satisfied. Many examples confirming the importance of such conditions were published in different journal articles which are often difficult to find. This book brings together examples based upon such sources, along with several new ones. In addition, it indicates the areas where Markov decision processes can be used. Active researchers can refer to this book on applicability of mathematical methods and theorems. It is also suitable reading for graduate and research students where they will better understand the theory.
Book Synopsis Markov Decision Processes and the Belief-Desire-Intention Model by : Gerardo I. Simari
Download or read book Markov Decision Processes and the Belief-Desire-Intention Model written by Gerardo I. Simari and published by Springer Science & Business Media. This book was released on 2011-09-18 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this work, we provide a treatment of the relationship between two models that have been widely used in the implementation of autonomous agents: the Belief DesireIntention (BDI) model and Markov Decision Processes (MDPs). We start with an informal description of the relationship, identifying the common features of the two approaches and the differences between them. Then we hone our understanding of these differences through an empirical analysis of the performance of both models on the TileWorld testbed. This allows us to show that even though the MDP model displays consistently better behavior than the BDI model for small worlds, this is not the case when the world becomes large and the MDP model cannot be solved exactly. Finally we present a theoretical analysis of the relationship between the two approaches, identifying mappings that allow us to extract a set of intentions from a policy (a solution to an MDP), and to extract a policy from a set of intentions.
Book Synopsis Constrained Markov Decision Processes by : Eitan Altman
Download or read book Constrained Markov Decision Processes written by Eitan Altman and published by Routledge. This book was released on 2021-12-17 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unified approach for the study of constrained Markov decision processes with a finite state space and unbounded costs. Unlike the single controller case considered in many other books, the author considers a single controller with several objectives, such as minimizing delays and loss, probabilities, and maximization of throughputs. It is desirable to design a controller that minimizes one cost objective, subject to inequality constraints on other cost objectives. This framework describes dynamic decision problems arising frequently in many engineering fields. A thorough overview of these applications is presented in the introduction. The book is then divided into three sections that build upon each other.
Book Synopsis Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces by : Aswin Raghavan
Download or read book Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces written by Aswin Raghavan and published by . This book was released on 2017 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov Decision Processes (MDPs) are the de-facto formalism for studying sequential decision making problems with uncertainty, ranging from classical problems such as inventory control and path planning, to more complex problems such as reservoir control under rainfall uncertainty and emergency response optimization for fire and medical emergencies. Most prior research has focused on exact and approximate solutions to MDPs with factored states, assuming a small number of actions. In contrast to this, many applications are most naturally modeled as having factored actions described in terms of multiple action variables. In this thesis we study domain-independent algorithms that leverage the factored action structure in the MDP dynamics and reward, and scale better than treating each of the exponentially many joint actions as atomic. Our contributions are three-fold based on three fundamental approaches to MDP planning namely exact solution using symbolic dynamic programming (DP), anytime online planning using heuristic search and online action selection using hindsight optimization. The first part is focused on deriving optimal policies over all states for MDPs whose state and action space are described in terms of multiple discrete random variables. In order to capture the factored action structure, we introduce new symbolic operators for computing DP updates over all states efficiently by leveraging the abstract and symbolic representation of Decision Diagrams. Addressing the potential bottleneck of diagrammatic blowup in these operators we present a novel and optimal policy iteration algorithm that emphasizes the diagrammatic compactness of the intermediate value functions and policies. The impact is seen in experiments on the well-studied problems of inventory control and system administration where our algorithm is able to exploit the increasing compactness of the optimal policy with increasing complexity of the action space. Under the framework of anytime planning, the second part expands the scalability of our approach to factored actions by restricting its attention to the reachable part of the state space. Our contribution is the introduction of new symbolic generalization operators that guarantee a more moderate use of space and time while providing non-trivial generalization. These operators yield anytime algorithms that guarantee convergence to the optimal value and action for the current world state, while guaranteeing bounded growth in the size of the symbolic representation. We empirically show that our online algorithm is successfully able to combine forward search from an initial state with backwards generalized DP updates on symbolic states. The third part considers a general class of hybrid (mixed discrete and continuous) state and action (HSA) MDPs. Whereas the insights from the above approaches are valid for hybrid MDPs as well, there are significant limitations inherent to the DP approach. Existing solvers for hybrid state and action MDPs are either limited to very restricted transition distributions, require knowledge of domain-specific basis functions to achieve good approximations, or do not scale. We explore a domain-independent approach based on the framework of hindsight optimization (HOP) for HSA-MDPs, which uses an upper bound on the finite-horizon action values for action selection. Our main contribution is a linear time reduction to a Mixed Integer Linear Program (MILP) that encodes the HOP objective, when the dynamics are specified as location-scale probability distributions parametrized by Piecewise Linear (PWL) functions of states and actions. In addition, we show how to use the same machinery to select actions based on a lower-bound generated by straight-line plans. Our empirical results show that the HSA-HOP approach effectively scales to high-dimensional problems and outperforms baselines that are capable of scaling to such large hybrid MDPs. In a concluding case study, we cast the real-time dispatch optimization problem faced by the Corvallis Fire Department as an HSA-MDP with factored actions. We show that our domain-independent planner significantly improves upon the responsiveness of the baseline that dispatches the nearest responders.
Book Synopsis A Concise Introduction to Decentralized POMDPs by : Frans A. Oliehoek
Download or read book A Concise Introduction to Decentralized POMDPs written by Frans A. Oliehoek and published by Springer. This book was released on 2016-06-03 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.
Book Synopsis Decision Making Under Uncertainty by : Mykel J. Kochenderfer
Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Book Synopsis Narrative-Centered Tutorial Planning with Concurrent Markov Decision Processes by : Jonathan Paul Rowe
Download or read book Narrative-Centered Tutorial Planning with Concurrent Markov Decision Processes written by Jonathan Paul Rowe and published by . This book was released on 2013 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt:
