A Loss Landscape Visualization Framework for Interpreting Reinforcement Learning: An ADHDP Case Study

• URL: http://arxiv.org/abs/2603.14600v1
• Date: Sun, 15 Mar 2026 20:47:30 GMT
• Title: A Loss Landscape Visualization Framework for Interpreting Reinforcement Learning: An ADHDP Case Study
• Abstract summary: Reinforcement learning algorithms have been widely used in dynamic and control systems.<n>This study extends that method into a framework which provides a multi-perspective view of the learning dynamics.<n>The framework is applied to compare several ADHDP variants and shows how training stabilizers and target updates change the optimization landscape.
• Score: 12.940253083606066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning algorithms have been widely used in dynamic and control systems. However, interpreting their internal learning behavior remains a challenge. In the authors' previous work, a critic match loss landscape visualization method was proposed to study critic training. This study extends that method into a framework which provides a multi-perspective view of the learning dynamics, clarifying how value estimation, policy optimization, and temporal-difference (TD) signals interact during training. The proposed framework includes four complementary components; a three-dimensional reconstruction of the critic match loss surface that shows how TD targets shape the optimization geometry; an actor loss landscape under a frozen critic that reveals how the policy exploits that geometry; a trajectory combining time, Bellman error, and policy weights that indicates how updates move across the surface; and a state-TD map that identifies the state regions that drive those updates. The Action-Dependent Heuristic Dynamic Programming (ADHDP) algorithm for spacecraft attitude control is used as a case study. The framework is applied to compare several ADHDP variants and shows how training stabilizers and target updates change the optimization landscape and affect learning stability. Therefore, the proposed framework provides a systematic and interpretable tool for analyzing reinforcement learning behavior across algorithmic designs.

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