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cs.RO2022
Reinforcement learning with Demonstrations from Mismatched Task under Sparse Reward
Yanjiang Guo, Jingyue Gao, Zheng Wu +2
Reinforcement learning often suffer from the sparse reward issue in real-world robotics problems. Learning from demonstration (LfD) is an effective way to eliminate this problem, w…
cs.RO2022
Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning
Zheng Wu, Yichen Xie, Wenzhao Lian +5
Humans are capable of abstracting various tasks as different combinations of multiple attributes. This perspective of compositionality is vital for human rapid learning and adaptio…