{
  "id": 3493,
  "url": "https://arxiv.org/abs/2605.30160v1",
  "title": "On Distributional Reinforcement Learning in Chaotic Dynamical Systems",
  "summary": "Chaotic dynamical systems pose a fundamental challenge for Reinforcement Learning (RL): exponential sensitivity to initial conditions induces high-variance bootstrap targets and poorly conditioned gradient updates. Chaotic dynamics arise across scientific and engineering domains, from fluid flows and climate systems to multi-agent systems, where reliable learning is highly desirable. Standard RL methods optimise expected returns through scalar value functions, implicitly averaging over diverging",
  "authors": "James Rudd-Jones, Mirco Musolesi, María Pérez-Ortiz",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-28T16:17:32.000Z",
  "fetched_at": "2026-07-14T16:30:18.854Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3493",
  "original_url": "https://arxiv.org/abs/2605.30160v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}