Knowledge reasoning is an important method to complement a knowledge graph, which aims to infer unknown facts or relations according to the existing knowledge in the graph. As the path information between entity pairs is not fully considered in most reasoning methods, the reasoning shows low efficiency and poor interpretability. To solve this problem, this study proposes TuckER embedding with reinforcement learning (TuckRL), a knowledge reasoning method that combines TuckER embedding and reinforcement learning (RL). First, entities and relations are mapped to low-dimensional vector space through TuckER embedding, and the path reasoning process is modeled using RL guided by strategies in the knowledge graph environment. Then, the action pruning mechanism is introduced to reduce the interference of invalid actions for action selection during path walking, and LSTM is used as the memory component to preserve the agent’s historical action trajectory. In this way, the agent can more accurately select valid actions and can complete knowledge reasoning by interaction with the knowledge graph. The experiments on three mainstream large-scale datasets indicate that TuckRL is superior to most of the existing methods, which demonstrates the effectiveness of combining embedding and RL for knowledge reasoning.