Robust Q-FTRL achieves ε-robust CCE in R-contaminated Markov games with H^3 S Σ_i A_i min{H,1/R}/ε^2 samples up to logs, matching a new lower bound; two-player zero-sum gives NE.
Markov games as a framework for multi-age nt reinforcement learning
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Minimax-Optimal Multi-Agent Robust Reinforcement Learning
Robust Q-FTRL achieves ε-robust CCE in R-contaminated Markov games with H^3 S Σ_i A_i min{H,1/R}/ε^2 samples up to logs, matching a new lower bound; two-player zero-sum gives NE.