10 citations · 50 across the 14 of their papers we have counts for
3 papers · 1 filter
Mastering Stacking of Diverse Shapes with Large-Scale Iterative Reinforcement Learning on Real Robots
Thomas Lampe, Abbas Abdolmaleki, Sarah Bechtle +12
Reinforcement learning solely from an agent's self-generated data is often believed to be infeasible for learning on real robots, due to the amount of data needed. However, if done…
Less is more -- the Dispatcher/ Executor principle for multi-task Reinforcement Learning
Martin Riedmiller, Andrea Gesmundo, Tim Hertweck +1
Humans instinctively know how to neglect details when it comes to solve complex decision making problems in environments with unforeseeable variations. This abstraction process see…
Replay across Experiments: A Natural Extension of Off-Policy RL
Dhruva Tirumala, Thomas Lampe, Jose Enrique Chen +9
Replaying data is a principal mechanism underlying the stability and data efficiency of off-policy reinforcement learning (RL). We present an effective yet simple framework to exte…