Web: http://arxiv.org/abs/2201.11685

Jan. 28, 2022, 2:11 a.m. | Weijun Hong, Menghui Zhu, Minghuan Liu, Weinan Zhang, Ming Zhou, Yong Yu, Peng Sun

cs.LG updates on arXiv.org arxiv.org

Exploration is crucial for training the optimal reinforcement learning (RL)
policy, where the key is to discriminate whether a state visiting is novel.
Most previous work focuses on designing heuristic rules or distance metrics to
check whether a state is novel without considering such a discrimination
process that can be learned. In this paper, we propose a novel method called
generative adversarial exploration (GAEX) to encourage exploration in RL via
introducing an intrinsic reward output from a generative adversarial network, …

arxiv exploration learning reinforcement learning

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