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

ArXi:2603.14535v1 Announce Type: cross Reinforcement learning has proven its power on various occasions. However, its performance is not always guaranteed when system dynamics change. Instead, it largely relies on users' empirical experience. For reinforcement learning algorithms with an actor-critic structure, the critic neural network reflects the approximation and optimization process in the RL algorithm. Analyzing the performance of the critic neural network helps to understand the mechanism of the algorithm.