496 citations · 1.3k across the 5 of their papers we have counts for
5 papers
Safe Exploration in Continuous Action Spaces
Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerik +3
We address the problem of deploying a reinforcement learning (RL) agent on a physical system such as a datacenter cooling unit or robot, where critical constraints must never be vi…
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…
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…
Deep Q-learning from Demonstrations
Todd Hester, Matej Vecerik, Olivier Pietquin +11
Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of dat…
Sim-to-Real Robot Learning from Pixels with Progressive Nets
Andrei A. Rusu, Mel Vecerik, Thomas Rothörl +3
Applying end-to-end learning to solve complex, interactive, pixel-driven control tasks on a robot is an unsolved problem. Deep Reinforcement Learning algorithms are too slow to ach…