Shangtong Zhang

collaborators

2 papers

cs.LG2025

Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features

Zixuan Xie, Xinyu Liu, Rohan Chandra +1

Linear TD( $λ$ ) is one of the most fundamental reinforcement learning algorithms for policy evaluation. Previously, convergence rates are typically established under the assumption…

cs.LG2025

Linear $Q$ -Learning Does Not Diverge in $L^{2}$ : Convergence Rates to a Bounded Set

Xinyu Liu, Zixuan Xie, Shangtong Zhang

$Q$ -learning is one of the most fundamental reinforcement learning algorithms. It is widely believed that $Q$ -learning with linear function approximation (i.e., linear $Q$ -learning…

Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features

Linear Q-Learning Does Not Diverge in L2: Convergence Rates to a Bounded Set

Linear $Q$ -Learning Does Not Diverge in $L^{2}$ : Convergence Rates to a Bounded Set