Handbook of Learning and Approximate Dynamic ProgrammingJennie Si
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Page 69
... depends on x and uk through Eq . ( 3.2 ) . All subsequent values of the state can be determined from xx and from the chosen control history , uk , .. uf - 1 . Therefore , the cost of operation from t onward can be minimized with respect ...
... depends on x and uk through Eq . ( 3.2 ) . All subsequent values of the state can be determined from xx and from the chosen control history , uk , .. uf - 1 . Therefore , the cost of operation from t onward can be minimized with respect ...
Page 213
... depends on the solutions to every other subpart . The internal policy for a behavior depends on its role in the greater task . Consider , for example , the behavior Go ( hall , bedroom2 ) in the grid - world prob- lem . Figure 8.2 shows ...
... depends on the solutions to every other subpart . The internal policy for a behavior depends on its role in the greater task . Consider , for example , the behavior Go ( hall , bedroom2 ) in the grid - world prob- lem . Figure 8.2 shows ...
Page 250
... depends on X. The limit r * depends on A because the underlying Bellman's equation also depends on A. Furthermore , TD 250 IMPROVED TD METHODS WITH LINEAR FUNCTION APPROXIMATION.
... depends on X. The limit r * depends on A because the underlying Bellman's equation also depends on A. Furthermore , TD 250 IMPROVED TD METHODS WITH LINEAR FUNCTION APPROXIMATION.
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