496 citations · 1k across the 25 of their papers we have counts for
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cs.AI2017★ 496 cited
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
Mel Vecerik, Todd Hester, Jonathan Scholz +7
We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards. We build upon the Deep Deterministic Policy Gradient (DDPG) algor…
cs.LG2017★ 114 cited
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation
Ivaylo Popov, Nicolas Heess, Timothy Lillicrap +7
Deep learning and reinforcement learning methods have recently been used to solve a variety of problems in continuous control domains. An obvious application of these techniques is…