{
  "abstract": "Control of plasma turbulence remains a long-standing challenge in magnetically confined fusion research. Here, we explore a reinforcement-learning (RL) approach for bidirectional control of the turbulence-zonal-flow transition in the modified Hasegawa-Wakatani system, a minimal model of electrostatic drift-wave turbulence. Within our model, a weak zonal drag damps the otherwise long-lived zonal structures, giving them a finite lifetime and restoring the drive-damping balance required for repeatable transitions within finite control episodes; meanwhile, actuation is applied through a spatially distributed Gaussian source field under a time-weighted budget constraint on actuation costs. The plasma model is then coupled to CNN-based soft actor-critic and twin-delayed deterministic policy-gradient agents through a GPU-native JAX solver that is highly optimized for fast online training. In the turbulence-suppression task, the learned budget-aware schedule achieves the lowest time-integrated turbulent flux for a given consumed budget, outperforming both constant and linearly decreasing baselines across all tested unseen initial conditions. In the inverse zonal-break task, the agent discovers an up-down antisymmetric actuation pattern that induces radial $E\\times B$ convection, disrupts the zonal structure, and sustains the turbulent state. A physics-informed warm-buffer initialization facilitates this discovery, as random exploration alone struggles to locate the narrow optimal manifold within the vast action space. These results demonstrate reinforcement learning as a practical trajectory optimizer for nonlinear plasma dynamics, such as turbulence control, and highlight the importance of physics guidance in such applications.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.27845",
  "author_total": 5,
  "authors": [
    {
      "name": "Luning Sun"
    },
    {
      "name": "Ben Zhu"
    },
    {
      "name": "Xin-Yang Liu"
    },
    {
      "name": "Deepak Akhare"
    },
    {
      "name": "Jian-Xun Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8n07yy0w3nmq7.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.27845",
  "fulltext_url": null,
  "id": "8n07yy0w3nmq7",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.27845",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.plasm-ph"
    ],
    [
      "openalex",
      "3106|Magnetic confinement fusion research"
    ]
  ],
  "openalex": "W7204796849",
  "pdf_url": "https://arxiv.org/pdf/2608.27845",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8n07yy0w3nmq7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "2608.27845",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.27845"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.plasm-ph",
      "subfield": "plasma"
    }
  ],
  "title": "Reinforcement-learning control of turbulence transition in the modified Hasegawa-Wakatani system",
  "title_text": "Reinforcement-learning control of turbulence transition in the modified Hasegawa-Wakatani system",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/8n07yy0w3nmq7",
  "venue": "arXiv",
  "versions": []
}