42 citations · 57 across the 8 of their papers we have counts for
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cs.LG2021★ 3 cited
Adaptive Adversarial Training for Meta Reinforcement Learning
Shiqi Chen, Zhengyu Chen, Donglin Wang
Meta Reinforcement Learning (MRL) enables an agent to learn from a limited number of past trajectories and extrapolate to a new task. In this paper, we attempt to improve the robus…
cs.CV2021★ 2 cited
Pareto Self-Supervised Training for Few-Shot Learning
Zhengyu Chen, Jixie Ge, Heshen Zhan +2
While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed…
cs.LG2021★ 1 cited
Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning
Jinxin Liu, Donglin Wang, Qiangxing Tian +1
It is of significance for an agent to learn a widely applicable and general-purpose policy that can achieve diverse goals including images and text descriptions. Considering such p…