  An iterative learning algorithm is presented for continuous-time
linear-quadratic optimal control problems where the system is externally
symmetric with unknown dynamics. Both finite-horizon and infinite-horizon
problems are considered. It is shown that the proposed algorithm is globally
convergent to the optimal solution and has some advantages over adaptive
dynamic programming, including being unbiased under noisy measurements and
having a relatively low computational burden. Numerical experiments show the
effectiveness of the results.
