We also take a review of the achievements of DRL in various Besides, DRL plays an important role in game artificial Policy gradient, and model-based algorithms, and compare their main techniquesĪnd properties. In this paper, we survey the progress of DRL methods, including value-based, Mechanism updates the policy to maximize the return with an end-to-end method. Generally, DRL agents receive high-dimensional inputs at each step, and makeĪctions according to deep-neural-network-based policies. Authors: Kun Shao, Zhentao Tang, Yuanheng Zhu, Nannan Li, Dongbin Zhao Download PDF Abstract: Deep reinforcement learning (DRL) has made great achievements since proposed.
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