REVIEW 1 cited by
Reinforcement learning for graph theory, II. Small Ramsey numbers
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
We describe here how the recent Wagner's approach for applying reinforcement learning to construct examples in graph theory can be used in the search for critical graphs for small Ramsey numbers. We illustrate this application by providing lower bounds for the small Ramsey numbers $R(K_{2,5}, K_{3,5})$, $R(B_3, B_6)$ and $R(B_4, B_5)$ and by improving the lower known bound for $R(W_5, W_7)$.
Forward citations
Cited by 1 Pith paper
-
Reinforcement learning for graph theory, Parallelizing Wagner's approach
A parallelized RL search over graphs produces three new counterexamples to conjectured Laplacian spectral radius bounds, alongside a speedup claim that is only weakly supported.
Discussion (0). Continue with ORCID to comment.