AINeutralarXiv – CS AI · 9h ago6/10
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R-GTD: A Geometric Analysis of Gradient Temporal-Difference Learning in Singular Regimes
Researchers propose R-GTD, a regularized gradient temporal-difference learning algorithm that maintains convergence guarantees even when the feature interaction matrix becomes singular—a practical limitation in existing GTD methods. The geometric analysis provides explicit error bounds and addresses a key stability challenge in off-policy reinforcement learning with function approximation.