Feb. 15, 2024, 5:43 a.m. | Guowei Xu, Ruijie Zheng, Yongyuan Liang, Xiyao Wang, Zhecheng Yuan, Tianying Ji, Yu Luo, Xiaoyu Liu, Jiaxin Yuan, Pu Hua, Shuzhen Li, Yanjie Ze, Hal D

cs.LG updates on arXiv.org arxiv.org

arXiv:2310.19668v2 Announce Type: replace
Abstract: Visual reinforcement learning (RL) has shown promise in continuous control tasks. Despite its progress, current algorithms are still unsatisfactory in virtually every aspect of the performance such as sample efficiency, asymptotic performance, and their robustness to the choice of random seeds. In this paper, we identify a major shortcoming in existing visual RL methods that is the agents often exhibit sustained inactivity during early training, thereby limiting their ability to explore effectively. Expanding upon this …

abstract algorithms arxiv continuous control cs.cv cs.lg current efficiency every identify paper performance progress random reinforcement reinforcement learning robustness sample tasks through type visual

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