Abstract: Temporal knowledge graph (TKG) reasoning aims to predict future events based on historical facts. However, existing reinforcement learning (RL)-based reasoning methods merely conduct path searches at the entity level, lacking high-level semantic and logical guidance for the reasoning process. Moreover, ontologies, as conceptual abstractions of graphs, have strong logicality, but their construction typically relies on static statistical rules and is difficult to adapt dynamically to varying data distributions. To effectively address these issues, this paper proposes ODRT, an ontology-graph dual-agent reinforcement learning method for TKG reasoning. Specifically, it jointly initializes the TKG ontology by leveraging both large language model (LLM) semantic embeddings and pretrained TKG reasoning model structural embeddings. Furthermore, it dynamically optimizes the ontology structure through learnable frequency weights during the end-to-end training process. Within the proposed dual-agent RL framework, the upper-level ontology agent performs concept-level walks on the constructed ontology graph, providing effective semantic navigation for the lower-level graph agent through carefully designed shared historical states. Meanwhile, the lower-level graph agent conducts fine-grained entity-level searches on the temporal graph under the constraint of hierarchical soft reward functions. Extensive experiments on four publicly available reasoning datasets demonstrate that ODRT achieves state-of-the-art performance among existing RL-based baselines and reaches the current state-of-the-art level on two encyclopedic datasets. However, on event-based datasets with heterogeneous entity behavior patterns and predictions that rely on historical reuse, its performance still lags behind that of partially embedded methods. Comprehensive experimental analyses further verify the significant advantages of the proposed ontology-guided mechanism and the dual-agent reinforcement learning framework.