Handbook of Learning and Approximate Dynamic ProgrammingJennie Si
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Page 41
... elements that can solve difficult learning control problems , IEEE Trans . SMC , vol . 13 , no . 5 , pp . 834-846 , 1983 . 27. P. Werbos , The elements of intelligence , Cybernetica ( Namur ) , no . 3 , 1968 . 28. A. Bryson and Y. C. Ho ...
... elements that can solve difficult learning control problems , IEEE Trans . SMC , vol . 13 , no . 5 , pp . 834-846 , 1983 . 27. P. Werbos , The elements of intelligence , Cybernetica ( Namur ) , no . 3 , 1968 . 28. A. Bryson and Y. C. Ho ...
Page 87
... elements of C as the actor parameters ( a ) and the elements of P as the critic parameters ( w ) , the DHP actor and critic are given by : - č ( xk , a 、) = −Ce ( Xk − XR ) , x ( xk , we ) = Pexk · ( 3.33 ) ( 3.34 ) Q , R. , and P ...
... elements of C as the actor parameters ( a ) and the elements of P as the critic parameters ( w ) , the DHP actor and critic are given by : - č ( xk , a 、) = −Ce ( Xk − XR ) , x ( xk , we ) = Pexk · ( 3.33 ) ( 3.34 ) Q , R. , and P ...
Page 88
... ( elements in A ) than there are equations ( error elements ) , the problem is underdetermined . Hence , even in this simple case there exist multiple alternatives for the design of the parameter - update module ( including least ...
... ( elements in A ) than there are equations ( error elements ) , the problem is underdetermined . Hence , even in this simple case there exist multiple alternatives for the design of the parameter - update module ( including least ...
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