2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL) / Institute of Electrical and Electronics Engineers.

Format:

Book

Author/Creator:

Institute of Electrical and Electronics Engineers, author, issuing body.

Language:

English

Subjects (All):

Programming languages (Electronic computers).

Physical Description:

1 online resource

Other Title:

2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

Adaptive Dynamic Programming And Reinforcement Learning

Place of Publication:

Piscataway, New Jersey : IEEE, 2013.

Summary:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781467359252

1467359254