25 citations · 73 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
Akita: A CPU scheduler for virtualized Clouds
Esmail Asyabi, Azer Bestavros, Renato Mancuso +2
Clouds inherit CPU scheduling policies of operating systems. These policies enforce fairness while leveraging best-effort mechanisms to enhance responsiveness of all schedulable en…
Observing the Invisible: Live Cache Inspection for High-Performance Embedded Systems
Dharmesh Tarapore, Shahin Roozkhosh, Steven Brzozowski +1
The vast majority of high-performance embedded systems implement multi-level CPU cache hierarchies. But the exact behavior of these CPU caches has historically been opaque to syste…