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

arXiv:2603.14535v2 Announce Type: replace-cross Abstract: 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...