We use deep reinforcement learning to explore a class of heterotic GUT models constructed from line bundle sums over Complete Intersection Calabi Yau (CICY) manifolds. We perform several experiments where A3C agents are trained to search for such models. These agents significantly outperform random exploration, in the most favourable settings by a factor of 1700 when it comes to finding unique models. Furthermore, we find evidence that the trained agents also outperform random walkers on new manifolds. We conclude that the agents detect hidden structures in the compactification data, which is partly of general nature. The experiments scale well with h^{(1, 1)}, and may thus provide the key to model building on CICYs with large h^{(1, 1)}.

中文翻译：

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使用杂种线束模型进行探索和利用

我们使用深度强化学习来探索一类由完全交叉卡拉比丘（CICY）流形上的线束总和构造的异质GUT模型。我们执行了几个实验，训练了A3C代理搜索这些模型。在寻找独特模型时，在最有利的环境下，这些代理的性能大大优于随机探索，其系数是1700。此外，我们发现有证据表明，受过训练的特工在新歧管上的性能也优于随机助步器。我们得出的结论是，代理商检测到压实数据中的隐藏结构，这部分是一般性的。实验以h ^（1，1）很好地缩放，因此可以为在具有大h ^{（1，1）的}CICY上建立模型提供关键。