Web: http://arxiv.org/abs/2205.05588

May 12, 2022, 1:11 a.m. | Zhiyuan Zhou, Cameron Allen, Kavosh Asadi, George Konidaris

cs.LG updates on arXiv.org arxiv.org

We study the action generalization ability of deep Q-learning in discrete
action spaces. Generalization is crucial for efficient reinforcement learning
(RL) because it allows agents to use knowledge learned from past experiences on
new tasks. But while function approximation provides deep RL agents with a
natural way to generalize over state inputs, the same generalization mechanism
does not apply to discrete action outputs. And yet, surprisingly, our
experiments indicate that Deep Q-Networks (DQN), which use exactly this type of
function …

ai arxiv deep gap learning q-learning

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