Inference of decision preferences through others' behavior observation is a crucial skill for artificial agents to collaborate with humans. While some attempts have taken in this realm, the inference speed and accuracy of current methods still need improvement. The main obstacle to achieving higher accuracy lies in the stochastic nature of human behavior, a consequence of the stochastic reward system underlying human decision-making. To address this, we propose the development of an instant inference network (IIN), surmising the partially observable agents' stochastic character. The agent's character is parameterized by weights assigned to reward components in reinforcement learning, resulting in a singular policy for each character. To train the IIN for inferring diverse characters, we develop a universal policy comprising a set of policies reflecting different characters. Once the IIN is trained to cover diverse characters using the universal policy, it can return character parameters instantly by receiving behavior trajectories. The simulation results confirm that the inference accuracy of the proposed solution outperforms state-of-the-art algorithms, despite having lower computational complexity.