An application of neural networks to a problem in knot theory and group theory (untangling braids)
arXiv:2206.05373v11 citationsh-index: 20
Originality Synthesis-oriented
AI Analysis
This work addresses a specific problem in knot theory and group theory, but it is incremental as it applies existing neural network methods to a new domain.
The authors tackled the problem of untangling braids up to length 20 and width 4 using reinforcement learning with feed-forward neural networks, achieving success in minimizing the number of Reidemeister moves required.
We report on our success on solving the problem of untangling braids up to length 20 and width 4. We use feed-forward neural networks in the framework of reinforcement learning to train the agent to choose Reidemeister moves to untangle braids in the minimal number of moves.