Web: http://arxiv.org/abs/2206.11489

June 24, 2022, 1:10 a.m. | Pihe Hu, Yu Chen, Longbo Huang

cs.LG updates on arXiv.org arxiv.org

We study reinforcement learning with linear function approximation where the
transition probability and reward functions are linear with respect to a
feature mapping $\boldsymbol{\phi}(s,a)$. Specifically, we consider the
episodic inhomogeneous linear Markov Decision Process (MDP), and propose a
novel computation-efficient algorithm, LSVI-UCB$^+$, which achieves an
$\widetilde{O}(Hd\sqrt{T})$ regret bound where $H$ is the episode length, $d$
is the feature dimension, and $T$ is the number of steps. LSVI-UCB$^+$ builds
on weighted ridge regression and upper confidence value iteration with a
Bernstein-type …

approximation arxiv function learning lg linear minimax reinforcement reinforcement learning

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