New DRRL Algorithm Achieves Finite-Time Convergence with Linear 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.

RANK_REASON The cluster contains an academic paper detailing a new algorithm and its theoretical convergence guarantees. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Bellman update
• Distributionally Robust Q-Learning
• Lipschitz function
• Markov Chains
• Q-function
• reinforcement learning
• Saptarshi Mandal

• paper
• other

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