  Online approximation of an optimal station keeping strategy for a fully
actuated six degrees-of-freedom autonomous underwater vehicle is considered.
The developed controller is an approximation of the solution to a two player
zero-sum game where the controller is the minimizing player and an external
disturbance is the maximizing player. The solution is approximated using a
reinforcement learning-based actor-critic framework. The result guarantees
uniformly ultimately bounded (UUB) convergence of the states and UUB
convergence of the approximated policies to the optimal polices without the
requirement of persistence of excitation.
