Web: http://arxiv.org/abs/2209.07376

Sept. 16, 2022, 1:12 a.m. | Fanghui Liu, Luca Viano, Volkan Cevher

cs.LG updates on arXiv.org arxiv.org

This paper provides a theoretical study of deep neural function approximation
in reinforcement learning (RL) with the $\epsilon$-greedy exploration under the
online setting. This problem setting is motivated by the successful deep
Q-networks (DQN) framework that falls in this regime. In this work, we provide
an initial attempt on theoretical understanding deep RL from the perspective of
function class and neural networks architectures (e.g., width and depth) beyond
the "linear" regime. To be specific, we focus on the value based …

approximation arxiv exploration function reinforcement reinforcement learning understanding

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