Derives novel high-dimensional concentration inequalities for vector-valued Markov chain martingales and applies them to TD learning for consistency guarantees matching asymptotic variance up to logs and O(T^{-1/4} log T) Gaussian approximation rate.
Step 1: findκ.Due to the Markovian property, the matrixV k is a function ofs k−1 for everyk∈[n]
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Uncertainty quantification for Markov chain induced martingales with application to temporal difference learning
Derives novel high-dimensional concentration inequalities for vector-valued Markov chain martingales and applies them to TD learning for consistency guarantees matching asymptotic variance up to logs and O(T^{-1/4} log T) Gaussian approximation rate.