Understanding Robustness to Observation Corruptions through Actor-Critic Information Specialization

Observation corruptions can disrupt reinforcement learning (RL) by altering an agent's inputs and subsequent decisions. However, how Actor-Critic architecture shapes the resulting information and output responses remains poorly understood. We study shared and decoupled PPO trained on clean observations across Procgen, DMC, and Robust Gymnasium. Our framework combines information-theoretic decompositions, representation-to-output sensitivity, and performance-difference relations. Paired diagnostics and closed-loop evaluations address four questions concerning control performance, information specialization, output sensitivity, and responses across corruption types. Decoupled PPO achieves higher corrupted returns in most evaluated tasks, while return-related specialization exhibits greater proportional erosion than action-related specialization. The results distinguish information retention from feature and output stability, supporting a joint assessment of robustness that accounts for corruption type. Our code is included in the supplementary material and will be released on GitHub.