Handbook of Learning and Approximate Dynamic Programming

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Contents
Foreword  1 
Reinforcement Learning and Its Relationship to Supervised Learning  47 
ModelBased Adaptive Critic Designs  65 
Guidance in the Use of Adaptive Critics for Control  97 
Direct Neural Dynamic Programming  125 
The Linear Programming Approach to Approximate Dynamic  153 
g Discussion  173 
Reinforcement Learning in Large HighDimensional State Spaces  179 
g Conclusion  279 
Hierarchical Approaches to Concurrency Multiagency  285 
Learning and Optimization From a System Theoretic Perspective  311 
Robust Reinforcement Learning Using IntegralQuadratic  337 
Supervised ActorCritic Reinforcement Learning  359 
NearOptimal Control Via Reinforcement Learning  407 
Multiobjective Control Problems by Reinforcement Learning  433 
Adaptive Critic Based Neural Network for ControlConstrained  463 
g Hierarchical Decision Making  203 
Hierarchical Remforcement Learning in Theory  209 
Hierarchical Remforcement Learning in Practice  217 
IntraBehavior Learmng  223 
Improved Temporal Difference Methods with Linear Function  235 
Approximate Dynamic Programming for HighDimensional  261 
Applications of Approximate Dynamic Programming in Power Systems  479 
Robust Reinforcement Learning for floating Ventilation  517 
Helicopter Flight Control Using Direct Neural Dynamic Programming  535 
Toward Dynamic Stochastic Optimal Power Flow  561 
Control Optimization Security and Selfhealing of Benchmark  599 
Common terms and phrases
ALSPE action network actor adaptive critic designs agent algorithm analysis applications approach approximate dynamic programming approximate LP Artificial Intelligence backpropagation Barto basis functions behavior Belhnan chapter computational constraints control law control problems convergence cost costtogo function critic network curse of dimensionality defined derivatives direct NDP discussed equation error estimate example Figure formulation function approximation fuzzy goal Heuristic hierarchical IEEE Trans implemented improve input Intelligence leaming learning algorithm linear programming Lyapunov function Machine Learning Markov chain Markov decision processes matrix methods minimize module neural network neurocontroller node nonlinear operating optimal control optimal policy output parameters performance Policy Gradient power system Proc Qleaming Qlearning recurrent reinforcement learning reward robot robust control sample Section simulation solution solving space stability stochastic structure supervised learning task TD(A Theorem trajectory transition update Utility function value function variables vector voltage weights Werbos