Handbook of Learning and Approximate Dynamic ProgrammingJennie Si
|
From inside the book
Results 1-3 of 67
Page 32
... described by Van Roy's chapter 10 in this book . It would be interesting to see whether Van Roy's approach could be made more powerful , by exploiting the nonlinear programming methods described in Momoh's chapter 22. It would also be ...
... described by Van Roy's chapter 10 in this book . It would be interesting to see whether Van Roy's approach could be made more powerful , by exploiting the nonlinear programming methods described in Momoh's chapter 22. It would also be ...
Page 277
... described in this chapter have evolved in the context of solving an array of very large - scale resource allocation problems . One of the challenges that we always face in the field of approximate dynamic programming is evaluating how ...
... described in this chapter have evolved in the context of solving an array of very large - scale resource allocation problems . One of the challenges that we always face in the field of approximate dynamic programming is evaluating how ...
Page 544
... described for direct NDP . That is , b ( m + 1 ) = b ( m ) + Ab ( m ) , Abi ( m ) - A ( m ) [ -9J ( m ) JJt ( m ) Ob , ( m ) ‚ i = 1 ... 7 , ( 21.6 ) ( 21.7 ) where A is the learning rate . For the four biases corresponding to the trim ...
... described for direct NDP . That is , b ( m + 1 ) = b ( m ) + Ab ( m ) , Abi ( m ) - A ( m ) [ -9J ( m ) JJt ( m ) Ob , ( m ) ‚ i = 1 ... 7 , ( 21.6 ) ( 21.7 ) where A is the learning rate . For the four biases corresponding to the trim ...
Contents
Foreword | 1 |
Reinforcement Learning and Its Relationship to Supervised Learning | 47 |
ModelBased Adaptive Critic Designs | 65 |
Copyright | |
20 other sections not shown
Other editions - View all
Common terms and phrases
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