The Optimal Control of Partially Observable Markov Processes
Title | The Optimal Control of Partially Observable Markov Processes PDF eBook |
Author | Edward Jay Sondik |
Publisher | |
Pages | |
Release | 1971 |
Genre | |
ISBN |
Optimal Control Limit Policy for a Partially Observable Markov Decision Process Model
Title | Optimal Control Limit Policy for a Partially Observable Markov Decision Process Model PDF eBook |
Author | Chong Ho Lee |
Publisher | |
Pages | 154 |
Release | 1994 |
Genre | |
ISBN |
Markov Decision Processes with Their Applications
Title | Markov Decision Processes with Their Applications PDF eBook |
Author | Qiying Hu |
Publisher | Springer Science & Business Media |
Pages | 305 |
Release | 2007-09-14 |
Genre | Business & Economics |
ISBN | 0387369511 |
Put together by two top researchers in the Far East, this text examines Markov Decision Processes - also called stochastic dynamic programming - and their applications in the optimal control of discrete event systems, optimal replacement, and optimal allocations in sequential online auctions. This dynamic new book offers fresh applications of MDPs in areas such as the control of discrete event systems and the optimal allocations in sequential online auctions.
Optimal Control of a Partially Observable Markov Chain
Title | Optimal Control of a Partially Observable Markov Chain PDF eBook |
Author | Abraham Nir |
Publisher | |
Pages | 56 |
Release | 1986 |
Genre | |
ISBN |
Reinforcement Learning
Title | Reinforcement Learning PDF eBook |
Author | Marco Wiering |
Publisher | Springer Science & Business Media |
Pages | 653 |
Release | 2012-03-05 |
Genre | Technology & Engineering |
ISBN | 3642276458 |
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.
Partially Observed Markov Decision Processes
Title | Partially Observed Markov Decision Processes PDF eBook |
Author | Vikram Krishnamurthy |
Publisher | Cambridge University Press |
Pages | 491 |
Release | 2016-03-21 |
Genre | Mathematics |
ISBN | 1107134609 |
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.
Measure-Valued Processes in the Control of Partially-Observable Stochastic Systems
Title | Measure-Valued Processes in the Control of Partially-Observable Stochastic Systems PDF eBook |
Author | Wendell H. Fleming |
Publisher | |
Pages | 30 |
Release | 1979 |
Genre | |
ISBN |
This paper is concerned with the optimal control of continuous-time Markov processes. The admissible control laws are based on white-noise corrupted observations of a function on the state processes. A 'separated' control problem is introduced, whose states are probability measures on the original state space. The original and separated control problems are related via the nonlinear filter equation. The existence of a minimum for the separated problem is established. Under more restrictive assumptions it is shown that the minimum expected cost for the separated problem equals the infimum of expected costs for the original problem with partially observed states.