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From the introductory.
The purpose of this booklet is to describe the basic process of money creation in a "fractional reserve" banking system. The approach taken illustrates the changes in bank balance sheets that occur when deposits in banks change as a result of monetary action by the Federal Reserve System -- the central bank of the United States. The relationships shown are based on simplifying assumptions. For the sake of simplicity, the relationships are shown as if they were mechanical, but they are not, as is described later in the booklet. Thus, they should not be interpreted to imply a close and predictable relationship between a specific central bank transaction and the quantity of money.
The introductory pages contain a brief general description of the characteristics of money and how the U.S. money system works. The illustrations in the following two sections describe two processes: first, how bank deposits expand or contract in response to changes in the amount of reserves supplied by the central bank; and second, how those reserves are affected by both Federal Reserve actions and other factors. A final section deals with some of the elements that modify, at least in the short run, the simple mechanical relationship between bank reserves and deposit money.
The book begins with a chapter on traditional methods of supervised learning, covering recursive least squares learning, mean square error methods, and stochastic approximation. Chapter 2 covers single agent reinforcement learning. Topics include learning value functions, Markov games, and TD learning with eligibility traces. Chapter 3 discusses two player games including two player matrix games with both pure and mixed strategies. Numerous algorithms and examples are presented. Chapter 4 covers learning in multi-player games, stochastic games, and Markov games, focusing on learning multi-player grid games?two player grid games, Q-learning, and Nash Q-learning. Chapter 5 discusses differential games, including multi player differential games, actor critique structure, adaptive fuzzy control and fuzzy interference systems, the evader pursuit game, and the defending a territory games. Chapter 6 discusses new ideas on learning within robotic swarms and the innovative idea of the evolution of personality traits.
? Framework for understanding a variety of methods and approaches in multi-agent machine learning.
? Discusses methods of reinforcement learning such as a number of forms of multi-agent Q-learning
? Applicable to research professors and graduate students studying electrical and computer engineering, computer science, and mechanical and aerospace engineering
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