5k citations · 11k across the 51 of their papers we have counts for
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Emergence of Locomotion Behaviours in Rich Environments
Nicolas Heess, Dhruva TB, Srinivasan Sriram +9
The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals. In practice, however, it is common to carefully…
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…
PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations
Rico Jonschkowski, Roland Hafner, Jonathan Scholz +1
We propose position-velocity encoders (PVEs) which learn---without supervision---to encode images to positions and velocities of task-relevant objects. PVEs encode a single image i…
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…