Temporal Difference Model-based Reinforcement Learning (TDMR) is a research area within machine learning and reinforcement learning that seeks to combine the strengths of model-based and model-free reinforcement learning approaches. In TDMR, agents learn a model of the environment dynamics to predict future states and rewards, while also using temporal difference learning methods to update value functions and policy decisions. This hybrid approach enables agents to learn more efficiently and effectively in complex and uncertain environments by leveraging the benefits of both model-based and model-free methods. TDMR has been applied to various domains, including robotics, game playing, and complex decision-making problems.