Finite-Time Convergence of Distributionally Robust Q-Learning with Linear Function Approximation
Researchers have developed a new algorithm for Distributionally Robust Reinforcement Learning (DRRL) that provides finite-time convergence guarantees even with linear function approximation. This algorithm addresses limitations in existing DRRL methods, which often require tabular settings or specific structural assumptions. The new approach combines a target-network with a dual function-approximation scheme, utilizing moment-tracking critics and suffix averaging to achieve convergence to the optimal robust Q-function. AI
IMPACT Provides theoretical guarantees for robust reinforcement learning, potentially improving agent performance in uncertain environments.