630 citations · 2.5k across the 54 of their papers we have counts for
3 papers · 2 filters
How to Train your Quadrotor: A Framework for Consistently Smooth and Responsive Flight Control via Reinforcement Learning
Siddharth Mysore, Bassel Mabsout, Kate Saenko +1
We focus on the problem of reliably training Reinforcement Learning (RL) models (agents) for stable low-level control in embedded systems and test our methods on a high-performance…
Regularizing Action Policies for Smooth Control with Reinforcement Learning
Siddharth Mysore, Bassel Mabsout, Renato Mancuso +1
A critical problem with the practical utility of controllers trained with deep Reinforcement Learning (RL) is the notable lack of smoothness in the actions learned by the RL polici…
Learning visual servo policies via planner cloning
Ulrich Viereck, Kate Saenko, Robert Platt
Learning control policies for visual servoing in novel environments is an important problem. However, standard model-free policy learning methods are slow. This paper explores plan…