TY - BOOK ID - 10225654 TI - 2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL) PY - 2013 SN - 1467359254 1467359246 PB - Piscataway, New Jersey : IEEE, DB - UniCat KW - Programming languages (Electronic computers) UR - https://www.unicat.be/uniCat?func=search&query=sysid:10225654 AB - 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. ER -