Handbook of Learning and Approximate Dynamic ProgrammingJennie Si
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Page 49
... output value y ; might be one of the 95 printable ascii characters . When , as in this case , the output values are discrete , ƒ is called a classifier and the discrete output values are called classes . Alternatively , in credit card ...
... output value y ; might be one of the 95 printable ascii characters . When , as in this case , the output values are discrete , ƒ is called a classifier and the discrete output values are called classes . Alternatively , in credit card ...
Page 447
... output variable , while { U1‚1‚U1‚2 ‚ ... ‚ U1‚P } is a term set of consequent of rule i , p is the number of terms in the consequent . It is noted that , the output consequent in ( 17.19 ) is not just a single constant term as in usual ...
... output variable , while { U1‚1‚U1‚2 ‚ ... ‚ U1‚P } is a term set of consequent of rule i , p is the number of terms in the consequent . It is noted that , the output consequent in ( 17.19 ) is not just a single constant term as in usual ...
Page 524
... output of the reinforcement - learning agent . It has learned to be silent ( an output of 0 ) for most time steps . It produces large outputs at set point changes and at several other time steps . The combined output of the RL / PI ...
... output of the reinforcement - learning agent . It has learned to be silent ( an output of 0 ) for most time steps . It produces large outputs at set point changes and at several other time steps . The combined output of the RL / PI ...
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