arXiv (All)
open access
RL unknotter, hard unknots and unknotting number
We develop a reinforcement learning pipeline for simplifying knot diagrams. A trained agent learns move proposals and a value heuristic for navigating Reidemeister moves. The pipeline applies to arbitrary knots and links; we test it on ``very hard'' unknot diagrams and, using diagram inflation, on $
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Emergence of Fibrations, Compression, and Symmetry Breaking in Artificial Neural Networks#822195
- History-Conditioned Flow Matching for Probabilistic Dynamics of Tendon-Driven Continuum Robots#1061294
- Inferring symbolic dynamics of chaotic flows from persistence#301322
- R-equivalence of quandle colorings and inner automorphisms#799357
- R-equivalence of quandle colorings and inner automorphisms#797934
Record · ID 986829
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive