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Juyao Wei, Zhenggang Lu, Zheng Yin and Zhipeng Jing
This paper presents a novel data-driven multiagent reinforcement learning (MARL) controller for enhancing the running stability of independently rotating wheels (IRW) and reducing wheel?rail wear. We base our active guidance controller on the multiagent ...
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Yihan Niu, Feixiang Zhu, Moxuan Wei, Yifan Du and Pengyu Zhai
Maritime Autonomous Surface Ships (MASS) are becoming of interest to the maritime sector and are also on the agenda of the International Maritime Organization (IMO). With the boom in global maritime traffic, the number of ships is increasing rapidly. The...
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Jue Ma, Dejun Ning, Chengyi Zhang and Shipeng Liu
Prioritized experience replay (PER) is an important technique in deep reinforcement learning (DRL). It improves the sampling efficiency of data in various DRL algorithms and achieves great performance. PER uses temporal difference error (TD-error) to mea...
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Hongjie Zhang, Cheng Qu, Jindou Zhang and Jing Li
Deep Reinforcement Learning (DRL) is a promising approach for general artificial intelligence. However, most DRL methods suffer from the problem of data inefficiency. To alleviate this problem, DeepMind proposed Prioritized Experience Replay (PER). Thoug...
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