Menglin Li, Xueqiang Gu, Chengyi Zeng and Yuan Feng
Reinforcement learning, as a branch of machine learning, has been gradually applied in the control field. However, in the practical application of the algorithm, the hyperparametric approach to network settings for deep reinforcement learning still follo...
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Björn Lindenberg, Jonas Nordqvist and Karl-Olof Lindahl
It is well known that ensemble methods often provide enhanced performance in reinforcement learning. In this paper, we explore this concept further by using group-aided training within the distributional reinforcement learning paradigm. Specifically, we ...
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Zheng Li, Xinkai Chen, Jiaqing Fu, Ning Xie and Tingting Zhao
With the development of electronic game technology, the content of electronic games presents a larger number of units, richer unit attributes, more complex game mechanisms, and more diverse team strategies. Multi-agent deep reinforcement learning shines ...
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Meysam Gheisarnejad and Mohammad-Hassan Khooban
While impressive progress has been already achieved in wide-bandgap (WBG) semiconductors such as 4H-SiC and GaN technologies, the lack of intelligent methodologies to control the gate drivers has prevented exploitation of the maximum potential of semicon...
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Rong Zhou, Zhisheng Zhang and Yuan Wang
Deep reinforcement learning is one of the research hotspots in artificial intelligence and has been successfully applied in many research areas; however, the low training efficiency and high demand for samples are problems that limit the application. Ins...
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