The work gives the first algorithms for general robust Markov games with linear function approximation whose sample complexity breaks the curse of multiagency for large state spaces in both generative and online settings.
Improved sample complexity bounds for distributionally robust reinforcement learning.arXiv preprint arXiv:2303.02783
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A two-time-scale stochastic approximation algorithm for approximate distributionally robust RL satisfies a central limit theorem at rate n^{-1/2} with characterized covariances.
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Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation
The work gives the first algorithms for general robust Markov games with linear function approximation whose sample complexity breaks the curse of multiagency for large state spaces in both generative and online settings.
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Central Limit Theorem for Two-Time-Scale Approximate Distributionally Robust RL
A two-time-scale stochastic approximation algorithm for approximate distributionally robust RL satisfies a central limit theorem at rate n^{-1/2} with characterized covariances.