Handbook of Learning and Approximate Dynamic ProgrammingJennie Si
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Page 312
... performance can be measured by the realization factor of the perturbation , and the total effect of the parameter change on the performance is then the sum of the realization factors of all the perturbations induced by the parameter ...
... performance can be measured by the realization factor of the perturbation , and the total effect of the parameter change on the performance is then the sum of the realization factors of all the perturbations induced by the parameter ...
Page 313
... performance sensitivities for systems with special structures . The sensitivity formula obtained is simpler than the ... performance potentials and introduce the two formulas for performance differences and performance gradients . In ...
... performance sensitivities for systems with special structures . The sensitivity formula obtained is simpler than the ... performance potentials and introduce the two formulas for performance differences and performance gradients . In ...
Page 326
... performance derivative ( 12.3 ) . For any P ' ( or any Q P ' - P ) and h , the performance derivative can be calculated by solving for π and g for the system with P. But for performance difference , π ' is needed for each P ' . = 2. At ...
... performance derivative ( 12.3 ) . For any P ' ( or any Q P ' - P ) and h , the performance derivative can be calculated by solving for π and g for the system with P. But for performance difference , π ' is needed for each P ' . = 2. At ...
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