AI Summary: Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search

This paper studies the problem of finding graphs that maximize the number of edges, while avoiding short cycles. It formulates graph generation as a reinforcement learning task, and compares methods like AlphaZero and tabu search. A key finding is that using a curriculum - building larger graphs from good smaller graphs - significantly improves performance. The work makes progress on an open problem in extremal graph theory.

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AI Summary: Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search

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