Ziyi Wang, Grady Williams, Evangelos Theodorou
Published 2018-07-16Version 1
In this paper we present an information theoretic approach to stochastic optimal control problems for systems with compound Poisson noise. We generalize previous work on information theoretic path integral control to discontinuous dynamics with compound Poisson noise and develop an iterative model predictive control (MPC) algorithm using importance sampling. The proposed algorithm is parallelizable and when implemented on a Graphical Processing Unit (GPU) can run in real time. We test the performance of the proposed algorithm in simulation for two control tasks using a cartpole system and a quadrotor. Our simulations demonstrate improved performance of the new scheme and indicate the importance of incorporating the statistical characteristics of stochastic disturbances in the computation of the stochastic optimal control policies.
Stochastic Minimum Principle for Partially Observed Systems Subject to Continuous and Jump Diffusion Processes and Driven by Relaxed Controls
Stochastic Optimal Control for Systems with Drifts of Bounded Variation: A Maximum Principle Approach
A learning-based approach to stochastic optimal control under reach-avoid constraint
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