mlsanityData to 7 Oct 2026

Learning Stochastic Parametric Differentiable Predictive Control Policies

arXiv 2203.01447 ↗First posted 2 Mar 2022cs.LG cs.SY eess.SYcited by 4

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Abstract (arXiv)

The problem of synthesizing stochastic explicit model predictive control policies is known to be quickly intractable even for systems of modest complexity when using classical control-theoretic methods. To address this challenge, we present a scalable alternative called stochastic parametric differentiable predictive control (SP-DPC) for unsupervised learning of neural control policies governing stochastic linear systems subject to nonlinear chance constraints. SP-DPC is formulated as a deterministic approximation to the stochastic parametric constrained optimal control problem. This formulation allows us to directly compute the policy gradients via automatic differentiation of the problem's value function, evaluated over sampled parameters and uncertainties. In particular, the computed expectation of the SP-DPC problem's value function is backpropagated through the closed-loop system rollouts parametrized by a known nominal system dynamics model and neural control policy which allows for direct model-based policy optimization. We provide theoretical probabilistic guarantees for policies learned via the SP-DPC method on closed-loop stability and chance constraints satisfaction. Furthermore, we demonstrate the computational efficiency and scalability of the proposed policy optimization algorithm in three numerical examples, including systems with a large number of states or subject to nonlinear constraints.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 45 references that are in the map. First posted on arXiv 2 Mar 2022.

Builds on

20 of 45 references.

References · 20

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8 more
  1. 2019Practical Reinforcement Learning of Stabilizing Economic MPCcited by 15
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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1.

4 papers.

  1. 2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systemscited by 28
  2. 2023Approximate non-linear model predictive control with safety-augmented neural networkscited by 9
  3. 2022Neural Lyapunov Differentiable Predictive Controlcited by 6
  4. 2024Three Pathways to Neurosymbolic Reinforcement Learning with Interpretable Model and Policy Networkscited by 1