Handbook of Learning and Approximate Dynamic ProgrammingNow entering its fourth edition, the market-leading Handbook of MRI Technique has been fully revised and updated to incorporate new technologies and developments essential to good practice. Written specifically for technologists and highly illustrated, it guides the uninitiated through scanning techniques and helps more experienced technologists to improve image quality. The first part of the book considers the main aspects of theory that relate to scanning and also includes practical tips on gating, equipment use, patient care and safety, and information on contrast media. The second half provides step-by-step instruction for examining each anatomical area, beginning with a basic anatomy section followed by sections on indications, patient positioning, equipment, artefacts and tips on optimizing image quality.
Handbook of MRI Technique continues to be the ideal support both for radiographers new to MRI and for regular users looking for information on alternative techniques and suggestions on protocol modifications. |
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Contents
Foreword | 1 |
Reinforcement Learning and Its Relationship to Supervised Learning | 47 |
ModelBased Adaptive Critic Designs | 65 |
Guidance in the Use of Adaptive Critics for Control | 97 |
Direct Neural Dynamic Programming | 125 |
The Linear Programming Approach to Approximate Dynamic | 153 |
g Discussion | 173 |
Reinforcement Learning in Large HighDimensional State Spaces | 179 |
g Conclusion | 279 |
Hierarchical Approaches to Concurrency Multiagency | 285 |
Learning and Optimization From a System Theoretic Perspective | 311 |
Robust Reinforcement Learning Using IntegralQuadratic | 337 |
Supervised ActorCritic Reinforcement Learning | 359 |
NearOptimal Control Via Reinforcement Learning | 407 |
Multiobjective Control Problems by Reinforcement Learning | 433 |
Adaptive Critic Based Neural Network for ControlConstrained | 463 |
g Hierarchical Decision Making | 203 |
Hierarchical Remforcement Learning in Theory | 209 |
Hierarchical Remforcement Learning in Practice | 217 |
IntraBehavior Learmng | 223 |
Improved Temporal Difference Methods with Linear Function | 235 |
Approximate Dynamic Programming for HighDimensional | 261 |
Applications of Approximate Dynamic Programming in Power Systems | 479 |
Robust Reinforcement Learning for floating Ventilation | 517 |
Helicopter Flight Control Using Direct Neural Dynamic Programming | 535 |
Toward Dynamic Stochastic Optimal Power Flow | 561 |
Control Optimization Security and Selfhealing of Benchmark | 599 |