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Аннотация
Save on expensive professionals with this trusted bestseller! Running your own business is pretty cool, but when it comes to the financial side—accounts and payroll, for instance—it's not so cool! That’s why millions of small business owners around the world count on QuickBooks to quickly and easily manage accounting and financial tasks and save big time on hiring expensive professionals. In a friendly, easy-to-follow style, small business guru and bestselling author Stephen L. Nelson checks off all your financial line-item asks, including how to track your profits, plan a perfect budget, simplify tax returns, manage inventory, create invoices, track costs, generate reports, and pretty much any other accounts and financial-planning task that turns up on your desk! Keep up with the latest QuickBooks changes Use QuickBooks to track profits and finances Balance your budget Back up your data safely The fully updated new edition of QuickBooks For Dummies takes the sweat (and the expense) out of cooking the books—and gives you more time to savor the results of your labors!
Аннотация
Discover the latest developments in multi-robot coordination techniques with this insightful and original resource Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms. You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field. Readers will discover cutting-edge techniques for multi-agent coordination, including: An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium Improving convergence speed of multi-agent Q-learning for cooperative task planning Consensus Q-learning for multi-agent cooperative planning The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning A modified imperialist competitive algorithm for multi-agent stick-carrying applications Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.
