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20172025
most citedLeveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

496 citations · 1k across the 25 of their papers we have counts for

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cs.LG2018★ 254 cited

Learning by Playing - Solving Sparse Reward Tasks from Scratch

Martin Riedmiller, Roland Hafner, Thomas Lampe +6

We propose Scheduled Auxiliary Control (SAC-X), a new learning paradigm in the context of Reinforcement Learning (RL). SAC-X enables learning of complex behaviors - from scratch -…