FALL 2009
Preface
Branching out from trading operations research roots of the 1950s, Markov finding processes (MDPs) have gained recognition in such diverse ?elds as ecology, economics, and communication engineering. These applications have been tended to(p) by many theoretical advances. Markov finale processes, also referred to as stochastic dynamic programming or stochastic program line problems, are models for sequential decision making when outcomes are uncertain. The Markov decision process model consists of decision epochs, states, actions, rewards, and renewal probabilities. Choosing an action in a state generates a reward and determines the state at the next decision epoch through a transition probability function. Policies or strategies are prescriptions of which action to choose downstairs any eventuality at every future decision epoch. Decision makers seek policies which are optimal in many sense. Chapter 1 introduces the Markov decision process model as a sequential decision model with actions, rewards, transitions and policies. We illustrate these concepts with some examples: an archive model, red-black gambling, optimal stopping, optimal control of queues, and the multi-armed bandit problem.
Chapter 2 deals with the ?nite panorama model and the principle of dynamic programming, backward induction. We also arena under which conditions optimal policies are monotone, i.e. nondecreasing or nonincreasing in the social club of the state space. In chapter 3 the discounted rewards over an in?nite horizion are studied. This results in the optimality equation and solution methods to solve this equation: policy iteration, linear programming, value iteration and modi?ed value iteration. Chapter 4 discusses the criterion of average rewards over an in?nite horizion, in the some general case. Firstly, polynomial algorithms are developed to classify MDPs as irreducible or communicating. The...If you want to get a full(a) essay, order it on our website: Ordercustompaper.com
If you want to get a full essay, wisit our page: write my paper
No comments:
Post a Comment