The authors derive a variance-aware regret bound of order d H^2 bar sigma_T sqrt(T) for multinomial logistic MDPs together with a matching lower bound, establishing minimax optimality.
Then for any algorithm, there exists an MNL mixture MDP such that E [RegT ] ≥ Ω BdH 2σT √ T
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Minimax Optimal Variance-Aware Regret Bounds for Multinomial Logistic MDPs
The authors derive a variance-aware regret bound of order d H^2 bar sigma_T sqrt(T) for multinomial logistic MDPs together with a matching lower bound, establishing minimax optimality.