Deep Reinforcement Learning with Weighted Q-Learning

20 Mar 2020 Andrea Cini Carlo D'Eramo Jan Peters Cesare Alippi

Overestimation of the maximum action-value is a well-known problem that hinders Q-Learning performance, leading to suboptimal policies and unstable learning. Among several Q-Learning variants proposed to address this issue, Weighted Q-Learning (WQL) effectively reduces the bias and shows remarkable results in stochastic environments... (read more)

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