Description: Abstract
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 Daumé III Furong Huang Huazhe Xu
Paper Twitter Code
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. Expandi