mlsanityData to 14 Oct 2026

Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning

arXiv 2406.19817 ↗First posted 28 Jun 2024eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

The identification of a mathematical dynamics model is a crucial step in the designing process of a controller. However, it is often very difficult to identify the system's governing equations, especially in complex environments that combine physical laws of different disciplines. In this paper, we present a new approach that allows identifying an ordinary differential equation by means of a physics-informed machine learning algorithm. Our method introduces a special neural network that allows exploiting prior human knowledge to a certain degree and extends it autonomously, so that the resulting differential equations describe the system as accurately as possible. We validate the method on a Duffing oscillator with simulation data and, additionally, on a cascaded tank example with real-world data. Subsequently, we use the developed algorithm in a model-based reinforcement learning framework by alternately identifying and controlling a system to a target state. We test the performance by swinging-up an inverted pendulum on a cart.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 34 references that are in the map. First posted on arXiv 28 Jun 2024.

Builds on

12 of 34 references.

References · 12

  1. 2018Neural Ordinary Differential Equationscited by 3,779
  2. 2017Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equationscited by 373
  3. 2020A Comprehensive Survey of Neural Architecture Search: Challenges and Solutionscited by 254
  4. 2019Benchmarking Model-Based Reinforcement Learningcited by 162
  5. 2018Learning Equations for Extrapolation and Controlcited by 156
  6. 2020Dissecting Neural ODEscited by 153
  7. 2021Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and controlcited by 137
  8. 2019Safe Reinforcement Learning Using Robust MPCcited by 85
  9. 2019Nonlinear System Identification: A User-Oriented Roadmapcited by 57
  10. 2022Deep Learning and Symbolic Regression for Discovering Parametric Equationscited by 23
  11. 2022Kalman-Bucy-Informed Neural Network for System Identificationcited by 3
  12. 2019The Use of Gaussian Processes in System Identificationcited by 2

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  2. 2022Neural Ordinary Differential Equations for Nonlinear System Identification
  3. 2023Semi-Supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach
  4. 2025Learning State-Space Models of Dynamic Systems from Arbitrary Data using Joint Embedding Predictive Architectures
  5. 2022Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network
  6. 2026Physics-Informed Policy Optimization via Analytic Dynamics Regularization
  7. 2020Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning
  8. 2025Achieving Precise and Reliable Locomotion with Differentiable Simulation-Based System Identification