2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL) /

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Adaptive (or Approximate) dynamic programming (ADP) is a general and effective approach for solving optimal control problems by adapting to uncertain environments over time ADP optimizes a user defined cost function with respect to an adaptive control law, conditioned on prior knowledge of the syste...

Ausführliche Beschreibung

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Weitere Titel:
2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning
Adaptive Dynamic Programming And Reinforcement Learning
Körperschaft:
Institute of Electrical and Electronics Engineers
Format:
Elektronisch E-Book
Sprache:
Englisch
Veröffentlicht:
Piscataway, New Jersey : IEEE, 2013.
Zusammenfassung:
Adaptive (or Approximate) dynamic programming (ADP) is a general and effective approach for solving optimal control problems by adapting to uncertain environments over time ADP optimizes a user defined cost function with respect to an adaptive control law, conditioned on prior knowledge of the system, and its state, in the presence of system uncertainties A numerical search over the present value of the control minimizes a nonlinear cost function forward in time providing a basis for real time, approximate optimal control The ability to improve performance over time subject to new or unexplored objectives or dynamics has made ADP an attractive approach in a number of application domains including optimal control and estimation, operation research, and computational intelligence ADP is viewed as a form of reinforcement learning based on an actor critic architecture that optimizes a user prescribed value online and obtains the resulting optimal control policy.
Umfang:
1 online resource
ISBN:
9781467359252
1467359254
Schlagworte:
Programming languages (Electronic computers)
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