Explore a comprehensive video explanation of the paper "AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control". Delve into the innovative approach of combining goal-achieving reinforcement learning with imitation learning using GANs to create realistic character movements. Learn about the problem statement, reward signals, motion priors from GANs, algorithm overview, reward engineering, and experimental results. Gain insights into how this method produces high-quality motions comparable to state-of-the-art tracking-based techniques while accommodating large datasets of unstructured motion clips. Discover how the system automatically composes disparate skills without requiring a high-level motion planner or task-specific annotations.