The one-stop reference on all aspects of the U.S. presidency, The Presidency A to Z, Fifth Edition is an authoritative and accessible volume providing all the basic information readers need to understand the executive branch. This new and extensively revised fifth edition features important new entries on Barack Obama, Michelle Obama, John McCain, Guantanamo Bay, and War in Afghanistan. It also includes updated entries on Campaign Finance, Iraq War, Presidents′ relationship with Congress, and many more. More 300 comprehensive, easy-to-read entries offer quick information and in-depth background on how the executive branch has responded to the challenges facing the nation. Readers will find: · Biographies of every president and many others important to the office · Explanations of broader concepts and powers relating to the presidency · Complete election coverage and analysis · Discussions of relations with Congress, the Supreme Court, the bureaucracy, political parties, the media, interest groups, and the public · Exploration of the policies of each president and their impact on U.S. and world history
Presents one hundred State of the Union addresses in their entirety, covering 1913 to 2006, with introductory notes on the historical and political context of each speech and the reaction to it.
Policy search is a subfield in reinforcement learning which focuses on finding good parameters for a given policy parametrization. It is well suited for robotics as it can cope with high-dimensional state and action spaces, one of the main challenges in robot learning. We review recent successes of both model-free and model-based policy search in robot learning. Model-free policy search is a general approach to learn policies based on sampled trajectories. We classify model-free methods based on their policy evaluation strategy, policy update strategy, and exploration strategy and present a unified view on existing algorithms. Learning a policy is often easier than learning an accurate forward model, and, hence, model-free methods are more frequently used in practice. However, for each sampled trajectory, it is necessary to interact with the robot, which can be time consuming and challenging in practice. Model-based policy search addresses this problem by first learning a simulator of the robot's dynamics from data. Subsequently, the simulator generates trajectories that are used for policy learning. For both model-free and model-based policy search methods, we review their respective properties and their applicability to robotic systems" --abstract (pages vii-viii).
Advances in Parallel Computing series presents the theory and use of of parallel computer systems, including vector, pipeline, array, fifth and future generation computers and neural computers. This volume features original research work, as well as accounts on practical experience with and techniques for the use of parallel computers.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.