Model based Path Integral Stochastic Control: A Bayesian Nonparametric Approach
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.
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