  We present a data-driven optimal control framework that can be viewed as a
generalization of the path integral (PI) control approach. We find iterative
feedback control laws without parameterization based on probabilistic
representation of learned dynamics model. The proposed algorithm operates in a
forward-backward manner which differentiate from other PI-related methods that
perform forward sampling to find optimal controls. Our method uses
significantly less samples to find optimal controls compared to other
approaches within the PI control family that relies on extensive sampling from
given dynamics models or trials on physical systems in model-free fashions. In
addition, the learned controllers can be generalized to new tasks without
re-sampling based on the compositionality theory for the linearly-solvable
optimal control framework. We provide experimental results on three different
systems and comparisons with state-of-the-art model-based methods to
demonstrate the efficiency and generalizability of the proposed framework.
