most citedBalance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertainty

5 citations · 5 across the 1 of their papers we have counts for

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1 paper

cs.LG2020★ 5 cited

Balance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertainty

Yongle Luo, Kun Dong, Lili Zhao +3

Efficient and effective learning is one of the ultimate goals of the deep reinforcement learning (DRL), although the compromise has been made in most of the time, especially for th…