Handbook of Learning and Approximate Dynamic ProgrammingJennie Si
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Page 168
... solution of the approximate LP and let รด be an optimal solution of the reduced LP . In order for the solution of the reduced LP to be meaningful , || J * - || 1 , c should close to || J * - || 1 , c . This is formalized as a requirement ...
... solution of the approximate LP and let รด be an optimal solution of the reduced LP . In order for the solution of the reduced LP to be meaningful , || J * - || 1 , c should close to || J * - || 1 , c . This is formalized as a requirement ...
Page 464
... solution can fail outside the domain of validity of the linearization process . Dynamic programming can handle a family of initial conditions for linear as well as nonlinear problems . The usual method of solution , however , is ...
... solution can fail outside the domain of validity of the linearization process . Dynamic programming can handle a family of initial conditions for linear as well as nonlinear problems . The usual method of solution , however , is ...
Page 565
... solution in a multi - year planning process that considers variation in system load , generation and network ... solution point should be consistent with that in the operating constraints . The three main problems faced are : the ...
... solution in a multi - year planning process that considers variation in system load , generation and network ... solution point should be consistent with that in the operating constraints . The three main problems faced are : the ...
Contents
Foreword | 1 |
Reinforcement Learning and Its Relationship to Supervised Learning | 47 |
ModelBased Adaptive Critic Designs | 65 |
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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