We study the problem of learning to reason in large scale knowledge graphs (KGs).More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path.In contrast to prior work, our approach includes a reward function that takes the accuracy, diversity, and efficiency into consideration.Experimentally, we show that our proposed method outperforms a path-ranking based algorithm and knowledge graph embedding methods on Freebase and Never-Ending Language Learning datasets.1