496 citations · 1.3k across the 5 of their papers we have counts for
3 papers · 1 filter
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…
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…