The transformation towards intelligence in various industries is creating more demand for intelligent and flexible products. In the field of robotics, learning-based methods are increasingly being applied, with the purpose of training robots to learn to deal with complex and changing external environments through data. In this context, reinforcement learning and imitation learning are becoming research hotspots with their respective characteristics. However, the two have their own limitations in some cases, such as the high cost of data acquisition for reinforcement learning. Moreover, it is difficult for imitation learning to provide perfect demonstrations. As a branch of imitation learning, interactive imitation learning aims at transferring human knowledge to the agent through interactions between the demonstrator and the robot, which alleviates the difficulty of teaching. This thesis implements IIL algorithms in four simulation scenarios and conducts extensive experiments, aiming at providing exhaustive information about IIL methods both in action space and state space as well as comparison with RL methods.