  This paper addresses the problem of learning the optimal control policy for a
nonlinear stochastic dynamical system with continuous state space, continuous
action space and unknown dynamics. This class of problems are typically
addressed in stochastic adaptive control and reinforcement learning literature
using model-based and model-free approaches respectively. Both methods rely on
solving a dynamic programming problem, either directly or indirectly, for
finding the optimal closed loop control policy. The inherent `curse of
dimensionality' associated with dynamic programming method makes these
approaches also computationally difficult.
  This paper proposes a novel decoupled data-based control (D2C) algorithm that
addresses this problem using a decoupled, `open loop - closed loop', approach.
First, an open-loop deterministic trajectory optimization problem is solved
using a black-box simulation model of the dynamical system. Then, a closed loop
control is developed around this open loop trajectory by linearization of the
dynamics about this nominal trajectory. By virtue of linearization, a linear
quadratic regulator based algorithm can be used for this closed loop control.
We show that the performance of D2C algorithm is approximately optimal.
Moreover, simulation performance suggests significant reduction in training
time compared to other state of the art algorithms.
