{
  "id": "2402.15592",
  "version": "v2",
  "published": "2024-02-23T20:19:06.000Z",
  "updated": "2025-06-20T12:18:30.000Z",
  "title": "Solving a class of stochastic optimal control problems by physics-informed neural networks",
  "authors": [
    "Zhe Jiao",
    "Wantao Jia",
    "Weiqiu Zhu"
  ],
  "comment": "8 pages",
  "categories": [
    "math.OC",
    "cs.LG"
  ],
  "abstract": "The aim of this work is to develop a deep learning method for solving high-dimensional stochastic control problems based on the Hamilton--Jacobi--Bellman (HJB) equation and physics-informed learning. Our approach is to parameterize the feedback control and the value function using a decoupled neural network with multiple outputs. We train this network by using a loss function with penalty terms that enforce the HJB equation along the sampled trajectories generated by the controlled system. More significantly, numerical results on various applications are carried out to demonstrate that the proposed approach is efficient and applicable.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2025-06-20T12:18:30.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "stochastic optimal control problems",
      "physics-informed neural networks",
      "solving high-dimensional stochastic control problems",
      "loss function"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 8,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}