This paper examines the evolution of character control techniques from classical inverse kinematics (IK) approaches—including closed-form solutions, Cyclic Coordinate Descent (CCD), and Jacobian-based methods—to modern deep reinforcement learning frameworks such as DeepMimic. We analyze the strengths and limitations of traditional IK systems, highlight how physics-based learning addresses their shortcomings, and discuss the implications of DeepMimic's approach to motion imitation, generalization, and dynamic skill execution.