Multi-hop question answering (QA) requires connecting entities and relations across multiple reasoning steps rather than relying solely on textual understanding. While large language models (LLMs) excel at semantic comprehension, they often produce ungrounded reasoning chains; conversely, purely graph-based approaches lack adaptability across domains. This paper introduces NeuroPath, a hybrid reasoning framework that integrates semantic understanding from LLMs with structured inference over Relational Graph Attention Networks (RGATs). NeuroPath employs a reinforcement learning-based retrieval policy to explore relational paths, coupled with an adaptive stopping mechanism that determines when sufficient evidence has been gathered. This design enables interpretable, verifiable, and efficient multi-hop reasoning. Experimental results on three standard benchmarks, HotpotQA, 2WikiMultihopQA, and MuSiQue, demonstrate that NeuroPath consistently outperforms LLM-only, retrieval-augmented, and hybrid approaches with LLMs and knowledge graph (KG) baselines in both accuracy and reasoning consistency. Our findings highlight the potential of combining neural graph reasoning with language-guided policies to achieve explainable and adaptive multi-hop inference.