GAMPO: Auditing and Optimizing LSTM Memory for Partially Observable Reinforcement Learning

Recurrent policies must preserve useful evidence while limiting sensitivity to nuisance history. Forget-gate products omit paths through coupled LSTM cell–hidden dynamics. We use the complete joint-state Jacobian to bound finite history perturbations and define a full-path memory tail. Filter contraction and decoder calibration translate this tail into belief and control errors; a terminal-decision refinement avoids unnecessary horizon factors in recall tasks. Gate-Aware Memory Policy Optimization (GAMPO) combines an exponential teacher with sensitivity, information-state, and projection losses. Its population regret decomposition separates representation, critic, and projection errors, with explicit conditions linking fitted losses to these terms. Four controlled POMDPs reveal substantial limitations: the reported model-free variant remains near chance on recall tasks and can underperform unregularized sequence baselines. Full-path influence improves over a forget-only proxy on active sensing, and an independent fast-refresh control shows a regret benefit over omission. Short-delay and exact-belief controls expose persistent learning limitations even when memory or representation demands are reduced. These findings support full-path sensitivity as an auditing tool and expose the additional requirements for effective memory regularization.