In adaptive query processing (AQP), the query plan is adjusted based on actual execution conditions. AQP has proven effective in dynamic querying environments, such as knowledge graphs (KGs) on the web. The technique known as eddies enables tuple-wise adaptivity by dynamically reordering query operators at runtime. Eddies operate under a predefined symbolic routing policy, which determines the next operator to process each tuple. Although various routing policies have been proposed, their effectiveness varies across queries, and choosing a suboptimal policy can significantly degrade performance. To address this challenge, we propose a neuro-symbolic AQP approach that combines representation learning and supervised learning to predict the optimal routing policy for a given query. Experimental results on synthetic and real-world KGs demonstrate that our method achieves high precision in predicting optimal policies, is efficient to train and use at inference time, and generalizes well to queries with constants not seen during training.