🤖 AI Summary
This work addresses the instability commonly observed in online reinforcement learning for dynamic systems, which often stems from the opaque optimization dynamics of the critic network. To this end, the authors propose a critic-matching loss landscape visualization method that projects the critic’s parameter trajectory onto a low-dimensional linear subspace, enabling the construction of a three-dimensional loss surface and a two-dimensional optimization path. The study introduces, for the first time, the concept of a critic-matching loss landscape along with quantitative metrics, and integrates these with a normalized system performance index to enable joint qualitative and quantitative analysis of the training process. Experiments on inverted pendulum and spacecraft attitude control tasks demonstrate that the method effectively reveals distinct loss landscape characteristics associated with stable convergence versus unstable learning, offering a novel tool for understanding and diagnosing online reinforcement learning behavior.
📝 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 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. To support systematic interpretation of such algorithms in dynamic control problems, this work proposes a critic match loss landscape visualization method for online reinforcement learning. The method constructs a loss landscape by projecting recorded critic parameter trajectories onto a low-dimensional linear subspace. The critic match loss is evaluated over the projected parameter grid using fixed reference state samples and temporal-difference targets. This yields a three-dimensional loss surface together with a two-dimensional optimization path that characterizes critic learning behavior. To extend analysis beyond visual inspection, quantitative landscape indices and a normalized system performance index are introduced, enabling structured comparison across different training outcomes. The approach is demonstrated using the Action-Dependent Heuristic Dynamic Programming algorithm on cart-pole and spacecraft attitude control tasks. Comparative analyses across projection methods and training stages reveal distinct landscape characteristics associated with stable convergence and unstable learning. The proposed framework enables both qualitative and quantitative interpretation of critic optimization behavior in online reinforcement learning.