Visualizing Critic Match Loss Landscapes for Interpretation of Online Reinforcement Learning Control Algorithms

Researchers have developed a new visualization method for analyzing critic neural networks in reinforcement learning algorithms by creating 3D loss landscapes from parameter trajectories. The approach enables both visual and quantitative interpretation of critic optimization behavior in online reinforcement learning, demonstrated on control tasks like cart-pole and spacecraft attitude control.