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Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-action pairs. Thus, various additional augmentations are proposed to kee
Externí odkaz:
http://arxiv.org/abs/2302.11312
Autor:
Huo, Yuying, Guo, Yilang, Wang, Jiakang, Xue, Huijie, Feng, Yujuan, Chen, Weizheng, Li, Xiangyu
Publikováno v:
In Journal of Genetics and Genomics June 2023