  Over the last few years, sampling-based stochastic optimal control (SOC)
frameworks have shown impressive performances in reinforcement learning (RL)
with applications in robotics. However, such approaches require a large amount
of samples from many interactions with the physical systems. To improve
learning efficiency, we present a novel model-based and data-driven SOC
framework based on path integral formulation and Gaussian processes (GPs). The
proposed approach learns explicit and time-varying optimal controls
autonomously from limited sampled data. Based on this framework, we propose an
iterative control scheme with improved applicability in higher-dimensional and
more complex control tasks. We demonstrate the effectiveness and efficiency of
the proposed framework using two nontrivial examples. Compared to
state-of-the-art RL methods, the proposed framework features superior control
learning efficiency.
