Handbook of Learning and Approximate Dynamic ProgrammingJennie Si
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Page xiii
... usually multi - scale , multi - component , distributed , dynamic systems . While advances in science and engineering have enabled us to design and build complex systems , comprehensive understanding of how to control and optimize them ...
... usually multi - scale , multi - component , distributed , dynamic systems . While advances in science and engineering have enabled us to design and build complex systems , comprehensive understanding of how to control and optimize them ...
Page 382
... ( usually incrementally ) . Depending on the problem setup and critic training mechanics , DAC derivatives may or may not be sufficiently accurate for successful RNN training . Both BPTT and DAC must also be equipped with a parameter ...
... ( usually incrementally ) . Depending on the problem setup and critic training mechanics , DAC derivatives may or may not be sufficiently accurate for successful RNN training . Both BPTT and DAC must also be equipped with a parameter ...
Page 434
... usually used . The Pareto optimal solutions are noninferior solutions among feasible solutions . The derivation of noninferior solutions is the major issue of the multiobjective optimization . The Multi - Objective Genetic Algorithm ...
... usually used . The Pareto optimal solutions are noninferior solutions among feasible solutions . The derivation of noninferior solutions is the major issue of the multiobjective optimization . The Multi - Objective Genetic Algorithm ...
Contents
Foreword | 1 |
Reinforcement Learning and Its Relationship to Supervised Learning | 47 |
ModelBased Adaptive Critic Designs | 65 |
Copyright | |
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action network actor adaptive critic designs agent algorithm analysis angle applications approach approximate dynamic programming approximate LP backpropagation behavior Bellman equation BPTT chapter computational constraints control law control problems convergence cost critic network curse of dimensionality defined derivatives DHP neurocontroller direct NDP equation error estimate example Figure formulation function approximation fuzzy goal gradient helicopter Heuristic hierarchical IEEE Trans implemented improve initial input iteration learning algorithms learning rate linear programming load Lyapunov function Machine Learning Markov decision processes methods micro-alternator minimize module neural network node nonlinear operating optimal control optimal policy optimization problem output parameters Pareto optimal performance PI controller power system Proc Q-learning reinforcement learning reward robot Section simulation solve space stability stochastic structure supervised learning task techniques Theorem trajectory transition update Utility function value function variables vector voltage weights Werbos