Abstract: The corridor's transmission limit is defined as the downscaled projection of the grid's security boundary onto the cut set of the corridor, which is essentially a complex mixed integer nonconvex nonlinear problem considering voltage-reactive power optimization and multiple types of stability constraints. Furthermore, the increasing integration of new energies into the grid further expands the corridor's transmission limit computational dimension, making it difficult to solve by using the traditional methods. To this end, a method for calculating the transmission limit of corridors based on the transferable reinforcement learning is proposed. In the first place, a hybrid integer model of transmission limit with the differential-algebraic equations is established, which takes into account the constraints related to the transient power angle, the voltage stability, and the reactive power resources like the capacitor switching. Subsequently, the model is transformed into a Markov decision process with the mixed integers, and a method of proximal policy optimization based on the mixed Categorical distribution is proposed. Ultimately, the policy distribution entropy is introduced to maximize the objective to ensure the transferability of the intelligent computing model in the unseen operating modes, realizing the fast calculation of the transmission limit of the corridors under the implementation of the operating modes or the boundary condition switching. The verification of the IEEE 39-node system shows that compared with the traditional meta-heuristic black-box optimization algorithm, the proposed method improves the calculation efficiency by 97.15% without sacrificing the accuracy.