Beyond Backpropagation: Navigating the Limits of Supervised Learning in RL

Standard backpropagation fails in environments without predefined ideal outputs, necessitating a shift toward policy gradients. This fundamental concept allows AI to learn through trial and error, making it indispensable for developing complex autonomous decision-making systems.