Claims the first O(1/T) global optimality guarantee for deep neural actor-critic methods in decentralized multi-agent reinforcement learning, but the central proof conflates Q-function TD errors with advantage functions.
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Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning
Claims the first O(1/T) global optimality guarantee for deep neural actor-critic methods in decentralized multi-agent reinforcement learning, but the central proof conflates Q-function TD errors with advantage functions.